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Sensors and Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 April 2015) | Viewed by 361502

Special Issue Editors


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Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece
Interests: complex networks; wireless systems; ad hoc and sensor networks; software-defined radios and software-defined networks; online social networks; network modeling and optimization; network economics; cyber–physical systems; internet of things; future internet research experimentation; resource orchestration; 5G/6G system design; system sustainability
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Engineering, University of Messina, 98122 Messina, ME, Italy
Interests: embedded systems; machine learning; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A smart city represents an improvement of today’s cities both functionally and structurally, that strategically utilizes many smart factors, such as Information and Communications Technology (ICT), to increase the city’s sustainable growth and strengthen city functions, while ensuring citizens’ quality of life and health. Cities can be viewed as a microcosm of “objects” with which citizens interact daily: street furniture, public buildings, transportation, monuments, public lighting and much more. Moreover, a continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. The Internet of Things (IoT) concept imagines all these objects being “smart”, connected to the Internet, and able to communicate with each other and with the external environment, interacting and sharing data and information. Each object in the IoT can be both the collector and distributor of information regarding mobility, energy consumption, air pollution as well as potentially offering cultural and tourist information. As a consequence, cyber and real worlds are strongly linked in a smart city. New services can be deployed when needed and evaluation mechanisms will be set up to assess the health and success of a smart city.

The aim of this Special Issue is to bring together innovative developments in areas related to sensors and smart cities, including, but not limited to:

  • computing and sensing infrastructures;
  • cost (of node, energy, development, deployment, maintenance);
  • communication (security, resilience, low energy);
  • adaptability (to environment, energy, faults);
  • data processing (on nodes, distributed, aggregation, discovery, big data);
  • self-learning (pattern discovery, prediction, auto-configuration);
  • deployment (cost, error prevention, localization);
  • maintenance (troubleshooting, recurrent costs);
  • applications (both new and enjoying new life);
  • smart users experience;
  • trust and privacy;
  • crowdsourcing, crowdsensing, participatory sensing;
  • cognition and awareness;
  • cyber-physical systems.

Both review articles and original research papers relating to sensors and smart cities are solicited. There is particular interest for papers with advances towards practical experiences and services overcoming the adoption barriers for sensors and smart cities.

Prof. Dr. Antonio Puliafito
Prof. Dr. Symeon Papavassiliou
Prof. Dr. Dario Bruneo
Guest Editors

Manuscript Submission Information

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Keywords

  • sensors
  • distributed computing
  • internet of things
  • interconnected objects
  • cyber-physical systems
  • complex networks
  • applications in smart cities
  • computing and sensing infrastructures

Published Papers (36 papers)

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1186 KiB  
Article
Development of a Conductivity Sensor for Monitoring Groundwater Resources to Optimize Water Management in Smart City Environments
by Lorena Parra, Sandra Sendra, Jaime Lloret and Ignacio Bosch
Sensors 2015, 15(9), 20990-21015; https://doi.org/10.3390/s150920990 - 26 Aug 2015
Cited by 72 | Viewed by 9633
Abstract
The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore [...] Read more.
The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore the use of sensors can be a good solution. Groundwater is very important as a water resource. Just in the USA, aquifers represent the water source for 50% of the population. However, aquifers are endangered due to the contamination. One of the most important parameters to monitor in groundwater is the salinity, as high salinity levels indicate groundwater salinization. In this paper, we present a specific sensor for monitoring groundwater salinization. The sensor is able to measure the electric conductivity of water, which is directly related to the water salinization. The sensor, which is composed of two copper coils, measures the magnetic field alterations due to the presence of electric charges in the water. Different salinities of the water generate different alterations. Our sensor has undergone several tests in order to obtain a conductivity sensor with enough accuracy. First, several prototypes are tested and are compared with the purpose of choosing the best combination of coils. After the best prototype was selected, it was calibrated using up to 30 different samples. Our conductivity sensor presents an operational range from 0.585 mS/cm to 73.8 mS/cm, which is wide enough to cover the typical range of water salinities. With this work, we have demonstrated that it is feasible to measure water conductivity using solenoid coils and that this is a low cost application for groundwater monitoring. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Distribution of cities with more than 1 Million of inhabitants.</p>
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<p>Electric circuit of the sensor.</p>
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<p>Electric circuit of the sensor.</p>
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<p>Picture of the test bench for one of the measurements.</p>
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<p>Example of possible behaviors of different prototypes.</p>
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<p>Induced voltages for best frequencies of prototypes from 1 to 4 at test 1.</p>
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<p>Induced voltages for best frequencies of prototypes from 5 to 9 at test 2.</p>
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<p>Induced voltages for best frequencies of prototypes from 5′ to 9′ at test 3.</p>
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<p>Induced voltages for best frequencies of prototypes 3, 11, 12 and 14 at tests 4 and 5.</p>
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<p>Example of containers of water that accomplish the minimum cell volume (<b>A</b>) and do not accomplish it (<b>B</b>).</p>
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<p>Results of the first test to find out the minimum cell volume.</p>
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<p>Results of the second test to find out the minimum cell volume.</p>
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<p>Representation of data of calibration process.</p>
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7467 KiB  
Article
Visual Sensing for Urban Flood Monitoring
by Shi-Wei Lo, Jyh-Horng Wu, Fang-Pang Lin and Ching-Han Hsu
Sensors 2015, 15(8), 20006-20029; https://doi.org/10.3390/s150820006 - 14 Aug 2015
Cited by 113 | Viewed by 13471
Abstract
With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring [...] Read more.
With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Flowchart of visual sensing for flood events. The system utilizes visual sensor technology to automatically analyze the monitoring image to obtain information regarding the field water level and runoff region. According to the analysis results of high-risk areas, the system automatically alerts the ultimate decision-makers, who will then choose suitable disaster reduction actions based on the geographic images and runoff data.</p>
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<p>Flowchart of visual sensing process for floods. Before image analysis, (<b>a</b>) check whether the camera’s field of view (FOV) is correct; (<b>b</b>) set up a virtual seed and markers; (<b>c</b>) determine whether to carry out waterbody detection using an event-based trigger; and (<b>d</b>) evaluate the water level fluctuation tendency; (<b>e</b>) Water level variation based on the computation of waterbody detection data combined with virtual markers.</p>
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<p>Flowchart of sensing triggered by using event-based rules. All remote monitoring images are subject to a two-stage screening: (1) quality analysis of images for screening of images that cannot be analyzed, and (2) surface texture analysis of the seeding region of interest (ROI) to verify the existence of a waterbody. Only images that are subject to the two-stage screening are used for the subsequent waterbody region detection.</p>
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<p>Principle of graph-based image segmentation (referred from [<a href="#B60-sensors-15-20006" class="html-bibr">60</a>]) based on an image graph and its components: (<b>a</b>) edges between the pixels and its weight, <math display="inline"> <semantics> <mrow> <msub> <mi>e</mi> <mrow> <mn>0</mn> <mi>i</mi> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>4</mn> </mrow> </semantics> </math> for any image pixel <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>; (<b>b</b>) relationships of the internal distance, <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>I</mi> <mi>N</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics> </math>, and mutual external distance, <math display="inline"> <semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>E</mi> <mi>X</mi> <mi>T</mi> </mrow> </msub> </mrow> </semantics> </math>, between two spanning tree elements, <math display="inline"> <semantics> <mrow> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mi>C</mi> <mi>j</mi> </msub> </mrow> </semantics> </math>, where <math display="inline"> <semantics> <mrow> <msub> <mi>w</mi> <mrow> <mi>k</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics> </math> is the absolute distance of the RGB vector between the two vertices and not the Euclidean distance in space.</p>
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<p>Real-time field images and analysis data of the flood level. In this case, only five virtual markers were set from the riverbed to the embankment top. (<b>a</b>–<b>h</b>) show the visual sensing results from screen shots every 50 min; and (<b>i</b>) the graph generated using the number of the virtual markers covered by the waterbody against time.</p>
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<p>Images with false diagnoses. (<b>a</b>–<b>b</b>) boundaries are caused by wave of large turbulence, as indicated by the arrow; (<b>c</b>) result of signal loss that occurred during transmission and compression; and (<b>d</b>) result of contrast enhancement of (<b>c</b>) by 50 times.</p>
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<p>Indirect measurement of water level of runoff stream. The ruler scale in the images shows the elevation. (<b>a</b>–<b>d</b>) show the visual sensing results from screen shots every 15 min; and (<b>e</b>) the graph generated using the number of the virtual markers covered by the waterbody against time.</p>
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<p>Water intrusion on specific ground measured using IR photography. (<b>a</b>–<b>d</b>) show the visual sensing results from screen shots every 50 min; and (<b>e</b>) the graph generated using the number of the virtual markers covered by the waterbody against time.</p>
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2002 KiB  
Article
On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks
by Stella Kafetzoglou, Giorgos Aristomenopoulos and Symeon Papavassiliou
Sensors 2015, 15(8), 19597-19617; https://doi.org/10.3390/s150819597 - 11 Aug 2015
Cited by 4 | Viewed by 4792
Abstract
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless [...] Read more.
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Data aggregation procedure in data gathering tree.</p>
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<p>Schematic representation of the AEM algorithm.</p>
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<p>Probability of successful delivery for <span class="html-italic">D</span> = 20 s.</p>
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<p>Probability of successful delivery for <span class="html-italic">D</span> = 40 s.</p>
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<p>Energy Consumption for <span class="html-italic">D</span> = 20 s.</p>
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<p>Energy Consumption for <span class="html-italic">D</span> = 40 s.</p>
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<p>Information gain for <span class="html-italic">D</span> = 40 s.</p>
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<p>Total aggregation gain as a function of network traffic.</p>
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<p>Probability of successful delivery of packets.</p>
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<p>Energy Consumption.</p>
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992 KiB  
Article
Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience
by Paolo Bellavista, Antonio Corradi, Luca Foschini and Raffaele Ianniello
Sensors 2015, 15(8), 18613-18640; https://doi.org/10.3390/s150818613 - 30 Jul 2015
Cited by 28 | Viewed by 6712
Abstract
Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of [...] Read more.
Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers’ behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Task state lifecycle. Data collection is enabled only in the running state. States with a bold stroke reached through transitions represented with a bold arrow are kept in sync between clients and the server.</p>
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<p>The architecture of the ParticipAct client.</p>
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<p>The MoST Architecture.</p>
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<p>The server-side architecture of ParticipAct.</p>
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<p>Screen capture of the ParticipAct Web Administration Interface. This figure shows the interactive page that allows to define the geo-notification area of a task.</p>
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<p>Number of candidates selected by each policy in case of geo-notified (<b>left</b>) and non-geo-notified (<b>right</b>) tasks.</p>
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<p>Accuracy on geo-notified (<b>left</b>) and non-geo-notified (<b>right</b>) tasks.</p>
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<p>Precision of geo-notified (<b>left</b>) and non-geo-notified (<b>right</b>) tasks.</p>
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<p>Acceptance (<b>a</b>) and completion (<b>b</b>) rate for different types of task.</p>
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<p>CCDF of acceptance (<b>a</b>) and completion (<b>b</b>) time (in seconds) for geo-notified and non-geo notified tasks.</p>
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<p>Ratio of success by users’ university course.</p>
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<p>Number of tasks completed by users.</p>
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<p>Point assignment strategies comparison.</p>
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<p>Number of users with a specific number of friends.</p>
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<p>Participation in administrator’s task and user’s task.</p>
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3165 KiB  
Article
V-Alert: Description and Validation of a Vulnerable Road User Alert System in the Framework of a Smart City
by Unai Hernandez-Jayo, Idoia De-la-Iglesia and Jagoba Perez
Sensors 2015, 15(8), 18480-18505; https://doi.org/10.3390/s150818480 - 29 Jul 2015
Cited by 26 | Viewed by 6610
Abstract
V-Alert is a cooperative application to be deployed in the frame of Smart Cities with the aim of reducing the probability of accidents involving Vulnerable Road Users (VRU) and vehicles. The architecture of V-Alert combines short- and long-range communication technologies in order to [...] Read more.
V-Alert is a cooperative application to be deployed in the frame of Smart Cities with the aim of reducing the probability of accidents involving Vulnerable Road Users (VRU) and vehicles. The architecture of V-Alert combines short- and long-range communication technologies in order to provide more time to the drivers and VRU to take the appropriate maneuver and avoid a possible collision. The information generated by mobile sensors (vehicles and cyclists) is sent over this heterogeneous communication architecture and processed in a central server, the Drivers Cloud, which is in charge of generating the messages that are shown on the drivers’ and cyclists’ Human Machine Interface (HMI). First of all, V-Alert has been tested in a simulated scenario to check the communications architecture in a complex scenario and, once it was validated, all the elements of V-Alert have been moved to a real scenario to check the application reliability. All the results are shown along the length of this paper. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Description of the simulated architecture.</p>
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<p>Bike reception delay.</p>
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<p>Vehicle reception delay.</p>
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<p>V-Alert sequence diagram.</p>
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<p>Description of the system architecture.</p>
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<p>V-Alert software architecture.</p>
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<p>Hardware set-up.</p>
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<p>Communication protocol between OBU, RSU, and the Drivers Cloud.</p>
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<p>Human Machine Interface onboard the vehicle.</p>
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<p>Cyclist application interface.</p>
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<p>Communication protocol between cyclists and the Drivers Cloud.</p>
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<p>Relative position determination.</p>
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<p>V-Alert testing scenario in the city of Bilbao.</p>
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<p>PDR measurements obtained.</p>
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<p>V-Alert application reliability.</p>
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2718 KiB  
Article
Electrochemical Impedance Sensors for Monitoring Trace Amounts of NO3 in Selected Growing Media
by Seyed Alireza Ghaffari, William-O. Caron, Mathilde Loubier, Charles-O. Normandeau, Jeff Viens, Mohammed S. Lamhamedi, Benoit Gosselin and Younes Messaddeq
Sensors 2015, 15(7), 17715-17727; https://doi.org/10.3390/s150717715 - 21 Jul 2015
Cited by 16 | Viewed by 7433
Abstract
With the advent of smart cities and big data, precision agriculture allows the feeding of sensor data into online databases for continuous crop monitoring, production optimization, and data storage. This paper describes a low-cost, compact, and scalable nitrate sensor based on electrochemical impedance [...] Read more.
With the advent of smart cities and big data, precision agriculture allows the feeding of sensor data into online databases for continuous crop monitoring, production optimization, and data storage. This paper describes a low-cost, compact, and scalable nitrate sensor based on electrochemical impedance spectroscopy for monitoring trace amounts of NO3 in selected growing media. The nitrate sensor can be integrated to conventional microelectronics to perform online nitrate sensing continuously over a wide concentration range from 0.1 ppm to 100 ppm, with a response time of about 1 min, and feed data into a database for storage and analysis. The paper describes the structural design, the Nyquist impedance response, the measurement sensitivity and accuracy, and the field testing of the nitrate sensor performed within tree nursery settings under ISO/IEC 17025 certifications. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>(<b>a</b>) Picture of the polyvinyl chloride-bis(2-ethylhexyl) phthalate (PVC-BEHP) electro-chemical nitrate sensor; (<b>b</b>) Schematics of the main electrical conduction paths, one within the polymer membrane and the other into the medium under test; and (<b>c</b>) Equivalent electrical circuit of the sensor.</p>
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<p>Real (<b>a</b>), Imaginary (<b>b</b>), Phase (<b>c</b>), and Nyquist (<b>d</b>) impedance spectra of the immersed PVC-BEHP electrochemical nitrate sensors, at 200 mV AC amplitude, through a wide range of nitrate (NO<sub>3</sub><b><sup>−</sup></b>) concentrations using KNO<sub>3</sub>-containing water solutions.</p>
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<p>Impedance spectra (Modulus and Phase) of the PVC-BEHP electro-chemical nitrate sensors, at 10 mV (green dots), 200 mV (red dots), and 1 V (black dots) AC amplitudes, showing the measurement dependency with respect to the applied AC amplitude.</p>
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<p>(Red dots) Nyquist response at 200 mV of the PVC-BEHP electro-chemical nitrate sensor through wide a range of nitrate (NO<sub>3</sub><b><sup>−</sup></b>) concentrations. (Green lines) Fitting results using the equivalent electrical circuit model illustrated in <a href="#sensors-15-17715-f001" class="html-fig">Figure 1</a>c.</p>
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<p>Comparative sensor impedance measurements made between the AD5933 microelectronics platform and the Solartron Impedance Analyzer.</p>
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<p>Field test results performed in growing medium selected from a white spruce tree nursery, showing the real part of the measured sensor impedance (in MΩ) at 1 kHz AC frequency compared against the ISO/IEC 17025-certified colorimetric NO<sub>3</sub><b><sup>−</sup></b> concentration measurements.</p>
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1149 KiB  
Article
Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities
by Günther Sagl, Bernd Resch and Thomas Blaschke
Sensors 2015, 15(7), 17013-17035; https://doi.org/10.3390/s150717013 - 14 Jul 2015
Cited by 68 | Viewed by 12115
Abstract
In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context [...] Read more.
In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today’s technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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Graphical abstract

Graphical abstract
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<p>Model of smart city interactions between humans, the environment, and technology. The interfaces (in orange) between humans, the environment and technology represent the interactions between these domains, which vary across spatial and temporal scales (right side of the figure); the context (blue) is a key component at the common intersection of these interactions.</p>
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<p>Dimensions involved in sensing (data generation, geographic phenomena, type of sensing), and some exemplary blocks (<b>a</b>–<b>f</b>) representing the amount of sensor data assigned to each dimension [<a href="#B140-sensors-15-17013" class="html-bibr">140</a>]. (<b>a</b>) VGI and mobile network traffic: associated with <span class="html-italic">in situ</span> sensing, social phenomena, and user-generated data; (<b>b</b>) VGI in the context of environmental status updates: associated with <span class="html-italic">in situ</span> sensing, physical phenomena, and user-generated data; (<b>c</b>) Satellite imagery: associated with remote sensing, physical phenomena, and machine-generated data; (<b>d</b>) Measurements from sensors and sensor networks: associated with <span class="html-italic">in situ</span> sensing, physical phenomena, and machine-generated data; (<b>e</b>) Human settlements extracted from satellite imagery: associated with remote sensing, social phenomena, and machine-generated data; (<b>f</b>) Numerical data at entrances to, and exits from shopping malls, public transport, <span class="html-italic">etc.</span>: associated with <span class="html-italic">in situ</span> sensing, social phenomena (e.g., mobility), and machine-generated data.</p>
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<p>Technology-enabled contextual sensing for smart cities: context-enriched human and technical geo-sensor information for smart cities (note: interaction interfaces between the environment, humans, and technology match those in <a href="#sensors-15-17013-f001" class="html-fig">Figure 1</a>, with emphasis placed on the sensing interface between the real world and the digital world).</p>
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1845 KiB  
Article
A Smart City Lighting Case Study on an OpenStack-Powered Infrastructure
by Giovanni Merlino, Dario Bruneo, Salvatore Distefano, Francesco Longo, Antonio Puliafito and Adnan Al-Anbuky
Sensors 2015, 15(7), 16314-16335; https://doi.org/10.3390/s150716314 - 6 Jul 2015
Cited by 27 | Viewed by 8760
Abstract
The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much [...] Read more.
The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Smart city as a closed-loop system.</p>
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<p>Google Maps and Google Earth screenshots of Albert Park in Auckland, New Zealand.</p>
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<p>Standard lamp used currently at the park <b>(Left);</b> possible replacement with a modern LED light <b>(Right).</b></p>
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<p>Data collection and inference/reaction subsystem: architecture.</p>
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<p>Maps screenshot: smart lampposts locations.</p>
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<p>Horizon-IoT panel: real-time graphs (no events).</p>
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<p>Horizon-IoT panel: lighting real-time graphs (including events).</p>
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<p>Horizon-IoT panel: noise/events real-time graphs (second half of the page).</p>
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12123 KiB  
Article
Providing IoT Services in Smart Cities through Dynamic Augmented Reality Markers
by David Chaves-Diéguez, Alexandre Pellitero-Rivero, Daniel García-Coego, Francisco Javier González-Castaño, Pedro Salvador Rodríguez-Hernández, Óscar Piñeiro-Gómez, Felipe Gil-Castiñeira and Enrique Costa-Montenegro
Sensors 2015, 15(7), 16083-16104; https://doi.org/10.3390/s150716083 - 3 Jul 2015
Cited by 27 | Viewed by 9398
Abstract
Smart cities are expected to improve the quality of life of citizens by relying on new paradigms, such as the Internet of Things (IoT) and its capacity to manage and interconnect thousands of sensors and actuators scattered across the city. At the same [...] Read more.
Smart cities are expected to improve the quality of life of citizens by relying on new paradigms, such as the Internet of Things (IoT) and its capacity to manage and interconnect thousands of sensors and actuators scattered across the city. At the same time, mobile devices widely assist professional and personal everyday activities. A very good example of the potential of these devices for smart cities is their powerful support for intuitive service interfaces (such as those based on augmented reality (AR)) for non-expert users. In our work, we consider a scenario that combines IoT and AR within a smart city maintenance service to improve the accessibility of sensor and actuator devices in the field, where responsiveness is crucial. In it, depending on the location and needs of each service, data and commands will be transported by an urban communications network or consulted on the spot. Direct AR interaction with urban objects has already been described; it usually relies on 2D visual codes to deliver object identifiers (IDs) to the rendering device to identify object resources. These IDs allow information about the objects to be retrieved from a remote server. In this work, we present a novel solution that replaces static AR markers with dynamic markers based on LED communication, which can be decoded through cameras embedded in smartphones. These dynamic markers can directly deliver sensor information to the rendering device, on top of the object ID, without further network interaction. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Dynamic LED marker.</p>
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<p>Effects of various sampling rates while sampling a signal [<a href="#b27-sensors-15-16083" class="html-bibr">27</a>].</p>
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<p>Intense light reflections may cause false positives <b>(Left)</b>. False positives are reduced by applying simple image processing techniques <b>(Right)</b>.</p>
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<p>Sampling points for fixed, blinking and data LEDs.</p>
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<p>Sampling sequence for dynamic augmented reality (AR) marker LEDs.</p>
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<p>A geometrical reference is passed to the application so it can determine the relative positions of the camera and the dynamic marker.</p>
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<p>Use case for system validation.</p>
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<p>Architecture model of the IoT infrastructure.</p>
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<p>Architecture model of the IoT infrastructure. Detail of mesh area network.</p>
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1829 KiB  
Article
Smart City Mobility Application—Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data
by Ivana Semanjski and Sidharta Gautama
Sensors 2015, 15(7), 15974-15987; https://doi.org/10.3390/s150715974 - 3 Jul 2015
Cited by 48 | Viewed by 8334
Abstract
Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in [...] Read more.
Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process). Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Distribution of trips (kilometres) made by mode (<b>Left</b>) and time of day (<b>Right</b>).</p>
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<p>(<b>a</b>) An example of the simple tree for the transportation mode bike; (<b>b</b>) An example of the simple tree for the transportation mode walk; (<b>c</b>) An example of the simple tree for the transportation mode car; (<b>d</b>) Average multinomial deviance for boosted trees.</p>
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<p>(<b>a</b>) An example of the simple tree for the transportation mode bike; (<b>b</b>) An example of the simple tree for the transportation mode walk; (<b>c</b>) An example of the simple tree for the transportation mode car; (<b>d</b>) Average multinomial deviance for boosted trees.</p>
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<p>Predictor variables importance plot.</p>
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<p>Classification matrix histogram.</p>
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773 KiB  
Article
On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model
by Antoine Bagula, Lorenzo Castelli and Marco Zennaro
Sensors 2015, 15(7), 15443-15467; https://doi.org/10.3390/s150715443 - 30 Jun 2015
Cited by 110 | Viewed by 21984
Abstract
Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid [...] Read more.
Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called “anchor” nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>The smart parking system.</p>
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<p>The smart parking sensor placement.</p>
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<p>The sensor placement model.</p>
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<p>Optimized Topologies Comparison. (<b>a</b>) Single-step Generated Topology; (<b>b</b>) Two-steps Generated Topology.</p>
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<p>Average Energy Consumption. (<b>a</b>) Randomly Generated Configuration; (<b>b</b>) Optimally Generated Configuration.</p>
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<p>Average Playback Delay. (<b>a</b>) Randomly Generated Configuration; (<b>b</b>) Optimally Generated Configuration.</p>
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<p>Average Packet Delivery. (<b>a</b>) Randomly Generated Configuration; (<b>b</b>) Optimally Generated Configuration.</p>
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607 KiB  
Article
Location Privacy for Mobile Crowd Sensing through Population Mapping
by Minho Shin, Cory Cornelius, Apu Kapadia, Nikos Triandopoulos and David Kotz
Sensors 2015, 15(7), 15285-15310; https://doi.org/10.3390/s150715285 - 29 Jun 2015
Cited by 18 | Viewed by 7601
Abstract
Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vicinity. [...] Read more.
Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users’ privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>An example tessellation of all access points (APs)</p>
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<p>A histogram of association counts for every minute of our dataset.</p>
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<p>A histogram of association counts for every minute for the access point with the most associations</p>
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<p>A (10, 0.7)-map generated for the 12 p.m.–1 p.m. time slot. Each colored region means that on 70% of the days, there were 10 or more unique associations between the hours of 12 p.m.–1 p.m. for each day between 22 September 2009 and 1 October 2009. The black dots correspond to AP locations.</p>
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<p>Target <span class="html-italic">k vs.</span> probability <span class="html-italic">p</span>.</p>
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<p>Target <span class="html-italic">k vs.</span> time slot start.</p>
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<p>Time slot duration <span class="html-italic">vs.</span> time slot start. (<b>a</b>) Average median <span class="html-italic">k</span>-accuracy; the line represents 95% <span class="html-italic">k</span>-accuracy; (<b>b</b>) average median cluster area; the line represents 1500 m<sup>2</sup>.</p>
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831 KiB  
Article
A Secure Routing Protocol for Wireless Sensor Networks Considering Secure Data Aggregation
by Triana Mugia Rahayu, Sang-Gon Lee and Hoon-Jae Lee
Sensors 2015, 15(7), 15127-15158; https://doi.org/10.3390/s150715127 - 26 Jun 2015
Cited by 29 | Viewed by 7125
Abstract
The commonly unattended and hostile deployments of WSNs and their resource-constrained sensor devices have led to an increasing demand for secure energy-efficient protocols. Routing and data aggregation receive the most attention since they are among the daily network routines. With the awareness of [...] Read more.
The commonly unattended and hostile deployments of WSNs and their resource-constrained sensor devices have led to an increasing demand for secure energy-efficient protocols. Routing and data aggregation receive the most attention since they are among the daily network routines. With the awareness of such demand, we found that so far there has been no work that lays out a secure routing protocol as the foundation for a secure data aggregation protocol. We argue that the secure routing role would be rendered useless if the data aggregation scheme built on it is not secure. Conversely, the secure data aggregation protocol needs a secure underlying routing protocol as its foundation in order to be effectively optimal. As an attempt for the solution, we devise an energy-aware protocol based on LEACH and ESPDA that combines secure routing protocol and secure data aggregation protocol. We then evaluate its security effectiveness and its energy-efficiency aspects, knowing that there are always trade-off between both. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>LEACH protocol.</p>
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<p>SLEACH protocol.</p>
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<p>SecLEACH protocol.</p>
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<p>MS-LEACH protocol.</p>
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<p>ESPDA protocol.</p>
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<p>Message flow of the proposed protocol.</p>
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<p>The proposed protocol.</p>
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<p>Relating each possible attack with the corresponding attacked security property.</p>
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6919 KiB  
Article
Citizen Sensors for SHM: Towards a Crowdsourcing Platform
by Ekin Ozer, Maria Q. Feng and Dongming Feng
Sensors 2015, 15(6), 14591-14614; https://doi.org/10.3390/s150614591 - 19 Jun 2015
Cited by 76 | Viewed by 8648
Abstract
This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen [...] Read more.
This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Previously, the authors have investigated the reliability of smartphone accelerometers for vibration-based SHM. This paper takes a step further to integrate mobile sensing and web-based computing for a prospective crowdsourcing-based SHM platform. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. A web-based platform was developed to collect and process the data automatically and store the processed data, such as modal properties of the structure, for long-term SHM purposes. Finally, the integrated mobile and web-based platforms were tested to collect the low-amplitude ambient vibration data of a bridge structure. Possible sources of uncertainties related to citizens were investigated, including the phone location, coupling conditions, and sampling duration. The field test results showed that the vibration data acquired by smartphones operated by citizens without expertise are useful for identifying structural modal properties with high accuracy. This platform can be further developed into an automated, smart, sustainable, cost-free system for long-term monitoring of structural integrity of spatially distributed urban infrastructure. Citizen Sensors for SHM will be a novel participatory sensing platform in the way that it offers hybrid solutions to transitional crowdsourcing parameters. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Integration scheme of system platforms.</p>
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<p>User login, recording, and submission screenshots, respectively.</p>
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<p>Digital signal processing operations applied on the server-side.</p>
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<p>Screenshot from the web interface showing the SHM results page.</p>
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<p>Inner and outer views, dimensions, and sensor layout of the pedestrian link bridge.</p>
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<p>1st, 2nd, and 3rd modal frequencies (8.46, 18.95, 29.67 Hz) and mode shapes from FDD by reference accelerometers.</p>
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<p>Acceleration time histories and Fourier spectra samples from Test 3 and Test 6.</p>
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<p>Fourier spectra from the average of 40 samples for Test 1–6.</p>
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<p>Identified frequencies obtained from different samples.</p>
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<p>Arias intensities obtained from different samples.</p>
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<p>Modal identification results from Test 1–6 and crowdsourcing.</p>
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<p>Acceleration time histories and Fourier spectra samples from pedestrian-induced vibrations.</p>
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972 KiB  
Article
Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems
by Athanasios Dasios, Damianos Gavalas, Grammati Pantziou and Charalampos Konstantopoulos
Sensors 2015, 15(6), 14487-14512; https://doi.org/10.3390/s150614487 - 18 Jun 2015
Cited by 45 | Viewed by 11500
Abstract
Older adults’ preferences to remain independent in their own homes along with the high costs of nursing home care have motivated the development of Ambient Assisted Living (AAL) technologies which aim at improving the safety, health conditions and wellness of the elderly. This [...] Read more.
Older adults’ preferences to remain independent in their own homes along with the high costs of nursing home care have motivated the development of Ambient Assisted Living (AAL) technologies which aim at improving the safety, health conditions and wellness of the elderly. This paper reports hands-on experiences in designing, implementing and operating UbiCare, an AAL based prototype system for elderly home care monitoring. The monitoring is based on the recording of environmental parameters like temperature and light intensity as well as micro-level incidents which allows one to infer daily activities like moving, sitting, sleeping, usage of electrical appliances and plumbing components. The prototype is built upon inexpensive, off-the-shelf hardware (e.g., various sensors, Arduino microcontrollers, ZigBee-compatible wireless communication modules) and license-free software, thereby ensuring low system deployment costs. The network comprises nodes placed in a house’s main rooms or mounted on furniture, one wearable node, one actuator node and a centralized processing element (coordinator). Upon detecting significant deviations from the ordinary activity patterns of individuals and/or sudden falls, the system issues automated alarms which may be forwarded to authorized caregivers via a variety of communication channels. Furthermore, measured environmental parameters and activity incidents may be monitored through standard web interfaces. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Plan view of the UbiCare deployment environment.</p>
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<p>Experimental testbed architecture of UbiCare.</p>
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<p>(<b>a</b>) The bathroom node; (<b>b</b>) the bedroom node; (<b>c</b>) the kitchen node; (<b>d</b>) magnetic contact switch mounted on the fridge; (<b>e</b>) the living room node; (<b>f</b>) the dining table chair’s force sensing resistor and node; (<b>g</b>) the wearable node; (<b>h</b>) the coordinator node.</p>
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<p>(<b>a</b>) The bathroom node; (<b>b</b>) the bedroom node; (<b>c</b>) the kitchen node; (<b>d</b>) magnetic contact switch mounted on the fridge; (<b>e</b>) the living room node; (<b>f</b>) the dining table chair’s force sensing resistor and node; (<b>g</b>) the wearable node; (<b>h</b>) the coordinator node.</p>
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<p>Illustration of the hardware components and wiring of the bedroom node.</p>
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<p>Schematic representation of the bedroom node’s pin connections.</p>
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<p>Visualization of activity monitoring; (<b>a</b>) Time spent (minutes per hour for a selected day) on bed; (<b>b</b>) toilet flush activation occurrences per day.</p>
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1983 KiB  
Article
Managing Emergency Situations in the Smart City: The Smart Signal
by Ángel Asensio, Teresa Blanco, Rubén Blasco, Álvaro Marco and Roberto Casas
Sensors 2015, 15(6), 14370-14396; https://doi.org/10.3390/s150614370 - 18 Jun 2015
Cited by 22 | Viewed by 8087
Abstract
In a city there are numerous items, many of them unnoticed but essential; this is the case of the signals. Signals are considered objects with reduced technological interest, but in this paper we prove that making them smart and integrating in the IoT [...] Read more.
In a city there are numerous items, many of them unnoticed but essential; this is the case of the signals. Signals are considered objects with reduced technological interest, but in this paper we prove that making them smart and integrating in the IoT (Internet of Things) could be a relevant contribution to the Smart City. This paper presents the concept of Smart Signal, as a device conscious of its context, with communication skills, able to offer the best message to the user, and as a ubiquitous element that contributes with information to the city. We present the design considerations and a real implementation and validation of the system in one of the most challenging environments that may exist in a city: a tunnel. The main advantages of the Smart Signal are the improvement of the actual functionality of the signal providing new interaction capabilities with users and a new sensory mechanism of the Smart City. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Smart Signal architecture.</p>
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<p>Operation cycle of Smart Signal.</p>
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<p><span class="html-italic">Smart Signal</span> concept applied to emergency signaling in tunnels. The Smart Signals provide indications adapted to the emergency characteristics.</p>
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<p>Render and signal prototype.</p>
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<p>Signal layers scheme: pictogram, retro-reflective, photo-luminescent, and LED layers.</p>
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<p>Block diagram of the electronic device.</p>
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<p>Electronic device.</p>
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<p>Smart Signal System infrastructure.</p>
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<p>Gateway software architecture.</p>
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<p>Uses of a <span class="html-italic">Smart Signal</span> with phosphorescent support.</p>
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<p>Distribution of routers and <span class="html-italic">Smart Signals</span> in Monrepos I tunnel.</p>
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<p>Details of setup around coordination station (scenario to test the perception).</p>
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<p>Snapshot during setup process. The closest signal (hanging on the wall) is turned off and the rest are turned on.</p>
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<p>Examples of scenarios for simulating an emergency (in each of them, the emergency exit is a virtual point only known to the evaluator.</p>
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<p>Routes and signals that depend on each router: (<b>a</b>) Initial snapshot; (<b>b</b>,<b>c</b>) simulating successive drop of nodes.</p>
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<p>Latency intervals (within a 95% interval) of signals.</p>
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Article
Analysis of Intelligent Transportation Systems Using Model-Driven Simulations
by Alberto Fernández-Isabel and Rubén Fuentes-Fernández
Sensors 2015, 15(6), 14116-14141; https://doi.org/10.3390/s150614116 - 15 Jun 2015
Cited by 32 | Viewed by 8760
Abstract
Intelligent Transportation Systems (ITSs) integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is [...] Read more.
Intelligent Transportation Systems (ITSs) integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is frequently unfeasible. Simulations help to address this problem, but present their own issues: there can be unintended mistakes in the transition from models to code; their platforms frequently bias modeling; and it is difficult to compare works that use different models and tools. In order to overcome these problems, this paper proposes a framework for a model-driven development of these simulations. It is based on a specific modeling language that supports the integrated specification of the multiple facets of an ITS: people, their vehicles, and the external environment; and a network of sensors and actuators conveniently arranged and distributed that operates over them. The framework works with a model editor to generate specifications compliant with that language, and a code generator to produce code from them using platform specifications. There are also guidelines to help researchers in the application of this infrastructure. A case study on advanced management of traffic lights with cameras illustrates its use. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Main components of the Model-Driven Engineering (MDE) framework for Intelligent Transportation System (ITS) simulation (in white) and related works (in yellow). Aggregation relationships (with diamonds) represent whole-part relations, and dependency relationships (discontinuous lines) different relations of use.</p>
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<p>Main concepts (in <span class="html-italic">italics</span>) of the ITS Modeling Language (ITSML) in context.</p>
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<p>Partial ITS metamodel. <span class="html-italic">Component</span> related elements.</p>
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<p>Partial ITS metamodel. <span class="html-italic">Place</span> and <span class="html-italic">Device</span> related elements.</p>
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<p>Partial ITS metamodel. <span class="html-italic">Person</span> and <span class="html-italic">Manager</span> related elements.</p>
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<p>Partial ITS metamodel. <span class="html-italic">Environment</span> related elements.</p>
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<p>Activity diagram of the development process with the ITSML. Concepts starting with uppercase belong to this ML.</p>
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<p>Intelligent traffic lights. <span class="html-italic">Container</span> related elements.</p>
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<p>Intelligent traffic lights. <span class="html-italic">Environment</span> related elements.</p>
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Article
Socially Aware Heterogeneous Wireless Networks
by Pavlos Kosmides, Evgenia Adamopoulou, Konstantinos Demestichas, Michael Theologou, Miltiades Anagnostou and Angelos Rouskas
Sensors 2015, 15(6), 13705-13724; https://doi.org/10.3390/s150613705 - 11 Jun 2015
Cited by 7 | Viewed by 5966
Abstract
The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order [...] Read more.
The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users’ locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>SDN-based system’s architecture.</p>
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<p>SN system—technology layer (deployment architecture).</p>
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<p>Architecture of a Probabilistic Neural Network.</p>
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<p>K-Means Clustering example with 2 clusters. (<b>a</b>) Initial clusters; (<b>b</b>) Final clusters.</p>
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<p>(<b>a</b>) Map of Chicago area, used for collecting data from Social Network; (<b>b</b>) Map of Chicago area divided into sub-areas.</p>
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<p>Social graph using Fruchterman-Reingold lay out algorithm s.</p>
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<p>Map of Chicago—<span class="html-italic">subarea-1</span>.</p>
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<p>Misclassification percentage for PNN with respect to σ values.</p>
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<p>Misclassification percentage per sub-area, (<b>a</b>) during morning periods; (<b>b</b>) during noon periods; (<b>c</b>) during afternoon periods; (<b>d</b>) during night periods.</p>
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<p>Misclassification percentage per sub-area, (<b>a</b>) during morning periods; (<b>b</b>) during noon periods; (<b>c</b>) during afternoon periods; (<b>d</b>) during night periods.</p>
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<p>Gap analysis for clustering sub-areas.</p>
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<p>Map of Chicago area divided into clusters for the night period.</p>
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<p>Misclassification percentage per cluster, (<b>a</b>) during morning periods; (<b>b</b>) during noon periods; (<b>c</b>) during afternoon periods; (<b>d</b>) during night periods.</p>
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4627 KiB  
Article
A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities
by Higinio Mora-Mora, Virgilio Gilart-Iglesias, David Gil and Alejandro Sirvent-Llamas
Sensors 2015, 15(6), 13591-13626; https://doi.org/10.3390/s150613591 - 10 Jun 2015
Cited by 47 | Viewed by 10720
Abstract
Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will [...] Read more.
Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Modelling with Eriksson-Penker notation of the process of obtaining citizen movements flows.</p>
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<p>Overall Computational architecture of the citizen track and trace system.</p>
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<p>Modelling with Eriksson-Penker notation of Design of the method for citizens’ localization acquisition process.</p>
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<p>RFID sensor network deployment.</p>
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<p>Installation and sensor coverage example.</p>
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<p>Modelling with Eriksson-Penker notation of the details of the process <span class="html-italic">Design of the method for communication and structuring of citizens’ location</span>.</p>
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<p>RFID smart sensor architecture.</p>
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<p>Location acquisition service architecture.</p>
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<p>RAML contract of location message acquisition service.</p>
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<p>Location acquisition service architecture.</p>
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<p>Citizen flow service RAML contract.</p>
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<p>RFID Smart Sensor prototype: (<b>a</b>) RFID Smart Sensor with antennas; (<b>b</b>) RFID reader with an embedded system.</p>
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<p>Mule flow. (<b>a</b>) Location message acquisition service; (<b>b</b>) Citizens flows generation service.</p>
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<p>Configuration of the antennas and coverage of RFID sensors: (<b>a</b>) One antenna on the right side of a sidewalk; (<b>b</b>) Three antennas on the right, left and top of a sidewalk.</p>
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<p>Temporal visualizations on the University Alicante campus with flow patterns: (<b>a</b>) temporal flow between 7.00 and 9.00 h; (<b>b</b>) temporal flow between 12.00 and 14.00 h</p>
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1561 KiB  
Article
Design and Development of nEMoS, an All-in-One, Low-Cost, Web-Connected and 3D-Printed Device for Environmental Analysis
by Francesco Salamone, Lorenzo Belussi, Ludovico Danza, Matteo Ghellere and Italo Meroni
Sensors 2015, 15(6), 13012-13027; https://doi.org/10.3390/s150613012 - 4 Jun 2015
Cited by 57 | Viewed by 8996
Abstract
The Indoor Environmental Quality (IEQ) refers to the quality of the environment in relation to the health and well-being of the occupants. It is a holistic concept, which considers several categories, each related to a specific environmental parameter. This article describes a low-cost [...] Read more.
The Indoor Environmental Quality (IEQ) refers to the quality of the environment in relation to the health and well-being of the occupants. It is a holistic concept, which considers several categories, each related to a specific environmental parameter. This article describes a low-cost and open-source hardware architecture able to detect the indoor variables necessary for the IEQ calculation as an alternative to the traditional hardware used for this purpose. The system consists of some sensors and an Arduino board. One of the key strengths of Arduino is the possibility it affords of loading the script into the board’s memory and letting it run without interfacing with computers, thus granting complete independence, portability and accuracy. Recent works have demonstrated that the cost of scientific equipment can be reduced by applying open-source principles to their design using a combination of the Arduino platform and a 3D printer. The evolution of the 3D printer has provided a new means of open design capable of accelerating self-directed development. The proposed nano Environmental Monitoring System (nEMoS) instrument is shown to have good reliability and it provides the foundation for a more critical approach to the use of professional sensors as well as for conceiving new scenarios and potential applications. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>(<b>a</b>) Assembled case with electronics; (<b>b</b>) Thermographic analysis.</p>
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<p>(<b>a</b>) nEMoS-App: variables as shown; (<b>b</b>) nEMoS-App: first part of the questionnaire.</p>
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<p>Residuals analysis of the temperatures.</p>
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<p>Residuals analysis of the relative humidity.</p>
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<p>Air temperature.</p>
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<p>Relative humidity of the air.</p>
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<p>Radiant temperature: indexes of position and variability of the data—Complete Series.</p>
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<p>Radiant temperature.</p>
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<p>Air speed velocity: verification phase.</p>
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<p>Air speed velocity: correction phase.</p>
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<p>Lighting: verification phase.</p>
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<p>Lighting: correction phase.</p>
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1282 KiB  
Article
A Low-Cost Sensing System for Cooperative Air Quality Monitoring in Urban Areas
by Simone Brienza, Andrea Galli, Giuseppe Anastasi and Paolo Bruschi
Sensors 2015, 15(6), 12242-12259; https://doi.org/10.3390/s150612242 - 26 May 2015
Cited by 73 | Viewed by 9745
Abstract
Air quality in urban areas is a very important topic as it closely affects the health of citizens. Recent studies highlight that the exposure to polluted air can increase the incidence of diseases and deteriorate the quality of life. Hence, it is necessary [...] Read more.
Air quality in urban areas is a very important topic as it closely affects the health of citizens. Recent studies highlight that the exposure to polluted air can increase the incidence of diseases and deteriorate the quality of life. Hence, it is necessary to develop tools for real-time air quality monitoring, so as to allow appropriate and timely decisions. In this paper, we present uSense, a low-cost cooperative monitoring tool that allows knowing, in real-time, the concentrations of polluting gases in various areas of the city. Specifically, users monitor the areas of their interest by deploying low-cost and low-power sensor nodes. In addition, they can share the collected data following a social networking approach. uSense has been tested through an in-field experimentation performed in different areas of a city. The obtained results are in line with those provided by the local environmental control authority and show that uSense can be profitably used for air quality monitoring. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>System architecture.</p>
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<p>(<b>a</b>) Libelium Gas Sensor Board. (<b>b</b>) A sensor node inside its PVC box.</p>
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<p>Webpage showing AQI levels for three sensor nodes.</p>
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<p>Mobile interface: initial page (<b>Left</b>) and nearest sensor nodes (<b>Right</b>).</p>
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<p>Locations of sensor nodes and control authority sensing station during in-field experimentation.</p>
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<p>Sensor installation in Zone A (<b>a</b>), Zone B (<b>b</b>), Zone B (<b>c</b>).</p>
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<p>Experimental results: gas concentrations (NO<sub>2</sub>, and CO) in the three zones.</p>
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<p>Experimental results: Air Quality Index (AQI) in the three zones.</p>
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<p>Experimental results: NO<sub>2</sub> values measured in Zone C and values provided by the local environmental control authority.</p>
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1212 KiB  
Article
User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm
by Serge Thomas Mickala Bourobou and Younghwan Yoo
Sensors 2015, 15(5), 11953-11971; https://doi.org/10.3390/s150511953 - 21 May 2015
Cited by 105 | Viewed by 11371
Abstract
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, [...] Read more.
This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>User activity recognition in smart home [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Architecture of hybrid method.</p>
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<p>Features of K-pattern algorithm.</p>
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<p>Architecture of tracking activity using K-pattern algorithm [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of forming frequent activity patterns [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of forming clustering [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Process of recomputing new center [<a href="#B1-sensors-15-11953" class="html-bibr">1</a>].</p>
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<p>Basic taxonomy of clustering algorithm.</p>
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<p>Multilayered artificial neural network.</p>
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<p>Training smart environment for activities recognition.</p>
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<p>Temporal relations representation.</p>
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1168 KiB  
Article
Branch-Based Centralized Data Collection for Smart Grids Using Wireless Sensor Networks
by Kwangsoo Kim and Seong-il Jin
Sensors 2015, 15(5), 11854-11872; https://doi.org/10.3390/s150511854 - 21 May 2015
Cited by 16 | Viewed by 5978
Abstract
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses [...] Read more.
A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Architecture of the advanced metering infrastructure (AMI) system.</p>
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<p>Example of a sensor network.</p>
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<p>Transmissions in breadth-first search.</p>
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<p>Generating and processing branches.</p>
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<p>Query and response flows.</p>
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<p>Algorithms for sink, internal and leaf node.</p>
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<p>Results of the proposed method and <a href="#FD11" class="html-disp-formula">Equation (5)</a>. (<b>a</b>) Tree structure; (<b>b</b>) Execution results of algorithms; (<b>c</b>) Number of leaf nodes, descendants and transmitted messages at each level.</p>
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<p>Success rate.</p>
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<p>Memory size.</p>
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1286 KiB  
Article
Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data
by Tobias Nef, Prabitha Urwyler, Marcel Büchler, Ioannis Tarnanas, Reto Stucki, Dario Cazzoli, René Müri and Urs Mosimann
Sensors 2015, 15(5), 11725-11740; https://doi.org/10.3390/s150511725 - 21 May 2015
Cited by 73 | Viewed by 9333
Abstract
Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, [...] Read more.
Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Starting with the (reformatted) raw data, a clustering further preprocessed the data before the actual classification was performed. Finally, the computed result was displayed.</p>
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<p>Additionally to the activities of daily living (ADL) classifier, a parallel visitor classifier was used. The results of the two classifiers were then merged.</p>
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<p>A token was calculated based on all passive infrared (PIR) values. Whenever the token changed (inactive states of all motion sensors were neglected), a change point was set. Periods between two change points were then compressed.</p>
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<p>To provide the classifier with contextual information about overlapping time periods, additional feature columns were introduced.</p>
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<p>Distribution of PIR recordings during 24 h of measurements for one volunteer. The x-axis shows the time of the day and the y-axis the normalized number of PIR recordings.</p>
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1678 KiB  
Article
A Multi-User Game-Theoretical Multipath Routing Protocol to Send Video-Warning Messages over Mobile Ad Hoc Networks
by Ahmad Mohamad Mezher, Mónica Aguilar Igartua, Luis J. De la Cruz Llopis, Esteve Pallarès Segarra, Carolina Tripp-Barba, Luis Urquiza-Aguiar, Jordi Forné and Emilio Sanvicente Gargallo
Sensors 2015, 15(4), 9039-9077; https://doi.org/10.3390/s150409039 - 17 Apr 2015
Cited by 7 | Viewed by 8479
Abstract
The prevention of accidents is one of the most important goals of ad hoc networks in smart cities. When an accident happens, dynamic sensors (e.g., citizens with smart phones or tablets, smart vehicles and buses, etc.) could shoot a video clip of the [...] Read more.
The prevention of accidents is one of the most important goals of ad hoc networks in smart cities. When an accident happens, dynamic sensors (e.g., citizens with smart phones or tablets, smart vehicles and buses, etc.) could shoot a video clip of the accident and send it through the ad hoc network. With a video message, the level of seriousness of the accident could be much better evaluated by the authorities (e.g., health care units, police and ambulance drivers) rather than with just a simple text message. Besides, other citizens would be rapidly aware of the incident. In this way, smart dynamic sensors could participate in reporting a situation in the city using the ad hoc network so it would be possible to have a quick reaction warning citizens and emergency units. The deployment of an efficient routing protocol to manage video-warning messages in mobile Ad hoc Networks (MANETs) has important benefits by allowing a fast warning of the incident, which potentially can save lives. To contribute with this goal, we propose a multipath routing protocol to provide video-warning messages in MANETs using a novel game-theoretical approach. As a base for our work, we start from our previous work, where a 2-players game-theoretical routing protocol was proposed to provide video-streaming services over MANETs. In this article, we further generalize the analysis made for a general number of N players in the MANET. Simulations have been carried out to show the benefits of our proposal, taking into account the mobility of the nodes and the presence of interfering traffic. Finally, we also have tested our approach in a vehicular ad hoc network as an incipient start point to develop a novel proposal specifically designed for VANETs. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>MPEG-2 GoP structure.</p>
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<p>Multipath routing scheme using three paths.</p>
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<p>PM and PMR packets.</p>
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<p>Proposed framework to send the video frames.</p>
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<p>Three possible allocation situations after playing the game. <span class="html-italic">F</span> and <span class="html-italic">M</span> represent the number of (I+P) and B frames to be sent, respectively. All the B frames are always sent through the worst path. (<b>a</b>) All the I+P frames are sent through the best path; (<b>b</b>) All the I+P frames are sent through the medium-quality path; (<b>c</b>) I+P frames will be sent through the best path with a certain probability <span class="html-italic">p</span> and through the second best path with a probability <span class="html-italic">1-p</span>. <span class="html-italic">F</span><sub>1</sub> and <span class="html-italic">F</span><sub>2</sub> represent the number of (I+P) frames sent through the best path and the medium-quality path, respectively, being <span class="html-italic">F</span> = <span class="html-italic">F</span><sub>1</sub> <span class="html-italic">+ F</span><sub>2</sub>.</p>
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<p>Subjective video quality measured by means of the mean opinion score (MOS) as a function of the fraction of packet losses (FPL).</p>
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<p>Best response probability <math display="inline"> <semantics id="sm139"> <mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mo>*</mo></msubsup></mrow></semantics></math> as a function of <span class="html-italic">k<sub>b/m</sub></span>, see <a href="#FD27" class="html-disp-formula">Equation (26)</a>.</p>
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<p>Average percentage of packet losses.</p>
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<p>Percentage of packet losses vs time (<span class="html-italic">N</span> = 3 users).</p>
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1391 KiB  
Article
Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection
by Wendong Xiao, Biao Song, Xiting Yu and Peiyuan Chen
Sensors 2015, 15(4), 8072-8087; https://doi.org/10.3390/s150408072 - 7 Apr 2015
Cited by 25 | Viewed by 5738
Abstract
Device-free localization (DFL) is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, [...] Read more.
Device-free localization (DFL) is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, city emergency response and infrastructure security. In DFL, wireless devices are used as sensors that can sense the target by transmitting and receiving wireless signals collaboratively. Many DFL systems are implemented based on received signal strength (RSS) measurements and the location of the target is estimated by detecting the changes of the RSS measurements of the wireless links. Due to the uncertainty of the wireless channel, certain links may be seriously polluted and result in erroneous detection. In this paper, we propose a novel nonlinear optimization approach with outlier link rejection (NOOLR) for RSS-based DFL. It consists of three key strategies, including: (1) affected link identification by differential RSS detection; (2) outlier link rejection via geometrical positional relationship among links; (3) target location estimation by formulating and solving a nonlinear optimization problem. Experimental results demonstrate that NOOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI) approach. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>DFL application scenario for aging at home.</p>
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<p>Affected links and outlier links.</p>
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<p>Distribution of the mapped distances.</p>
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<p>Possible location area of the target.</p>
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<p>The experimental setup.</p>
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<p>The errors when γ changes.</p>
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<p>Average errors for different γ</p>
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<p>Average errors for the different thresholds of variance (δ).</p>
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<p>Performance comparison of NOOLR, NOwoOLR, and RTI.</p>
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4608 KiB  
Article
Design of a Hybrid (Wired/Wireless) Acquisition Data System for Monitoring of Cultural Heritage Physical Parameters in Smart Cities
by Fernando-Juan García Diego, Borja Esteban and Paloma Merello
Sensors 2015, 15(4), 7246-7266; https://doi.org/10.3390/s150407246 - 25 Mar 2015
Cited by 26 | Viewed by 7930
Abstract
Preventive conservation represents a working method and combination of techniques which helps in determining and controlling the deterioration process of cultural heritage in order to take the necessary actions before it occurs. It is acknowledged as important, both in terms of preserving and [...] Read more.
Preventive conservation represents a working method and combination of techniques which helps in determining and controlling the deterioration process of cultural heritage in order to take the necessary actions before it occurs. It is acknowledged as important, both in terms of preserving and also reducing the cost of future conservation measures. Therefore, long-term monitoring of physical parameters influencing cultural heritage is necessary. In the context of Smart Cities, monitoring of cultural heritage is of interest in order to perform future comparative studies and load information into the cloud that will be useful for the conservation of other heritage sites. In this paper the development of an economical and appropriate acquisition data system combining wired and wireless communication, as well as third party hardware for increased versatility, is presented. The device allows monitoring a complex network of points with high sampling frequency, with wired sensors in a 1-wire bus and a wireless centralized system recording data for monitoring of physical parameters, as well as the future possibility of attaching an alarm system or sending data over the Internet. This has been possible with the development of three board’s designs and more than 5000 algorithm lines. System tests have shown an adequate system operation. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Restored flag in the display cabinet, Blasco Ibáñez Museum-House (Valencia).</p>
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<p>Master schematic final design.</p>
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<p>Slave Schematic Final Design.</p>
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<p>Wireless Schematic Final Design.</p>
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<p>(<b>a</b>) Master PCB Final Design; (<b>b</b>) Slave PCB Final Design; (<b>c</b>) Wireless PCB Final Design.</p>
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<p>Wireless module, I<sup>2</sup>C Support Orders.</p>
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<p>Flowchart of wireless orders from Master module.</p>
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<p>Flowchart of wireless orders from Wireless module.</p>
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<p>Flowchart of wireless orders from Slave module.</p>
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<p>Global communication example.</p>
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<p>Trajectories of sensor inside the display cabinet (red), sensor at the outside of the display cabinet (blue) and control data-logger at the outside of the display cabinet (green). (<b>a</b>) Temperature; (<b>b</b>) RH.</p>
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<p>Mean daily trajectories of sensor inside the display cabinet (red), sensor at the outside of the display cabinet (blue) and control data-logger at the outside of the display cabinet (green). (<b>a</b>) Temperature; (<b>b</b>) RH.</p>
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Article
Opportunistic Mobility Support for Resource Constrained Sensor Devices in Smart Cities
by Daniel Granlund, Patrik Holmlund and Christer Åhlund
Sensors 2015, 15(3), 5112-5135; https://doi.org/10.3390/s150305112 - 2 Mar 2015
Cited by 7 | Viewed by 7125
Abstract
A multitude of wireless sensor devices and technologies are being developed and deployed in cities all over the world. Sensor applications in city environments may include highly mobile installations that span large areas which necessitates sensor mobility support. This paper presents and validates [...] Read more.
A multitude of wireless sensor devices and technologies are being developed and deployed in cities all over the world. Sensor applications in city environments may include highly mobile installations that span large areas which necessitates sensor mobility support. This paper presents and validates two mechanisms for supporting sensor mobility between different administrative domains. Firstly, EAP-Swift, an Extensible Authentication Protocol (EAP)-based sensor authentication protocol is proposed that enables light-weight sensor authentication and key generation. Secondly, a mechanism for handoffs between wireless sensor gateways is proposed. We validate both mechanisms in a real-life study that was conducted in a smart city environment with several fixed sensors and moving gateways. We conduct similar experiments in an industry-based anechoic Long Term Evolution (LTE) chamber with an ideal radio environment. Further, we validate our results collected from the smart city environment against the results produced under ideal conditions to establish best and real-life case scenarios. Our results clearly validate that our proposed mechanisms can facilitate efficient sensor authentication and handoffs while sensors are roaming in a smart city environment. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>The Sense Smart City Architecture.</p>
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<p>EduRoam tree-like interconnection of AAA servers with different administrative domains.</p>
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<p>The EAP-Swift protocol authentication and session encryption key generation steps, including message exchange.</p>
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<p>Sensor node connection states and state transitions when connecting to a new sensor gateway.</p>
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<p>Experimental setup for sensor node power consumption measurement during the authentication procedure.</p>
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<p>Measured total authentication time for the EAP-Swift implementations based on MD5 and SHA1.</p>
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<p>Current consumption by the sensor node during one full authentication procedure using the MD5-based version of EAP-Swift.</p>
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<p>Experimental setup for evaluating the proposed handoff mechanism, with the sensor node roaming between different ADs.</p>
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<p>City environment experimental setup with fixed sensor nodes and mobile gateways connected to the cellular network (3G/LTE).</p>
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<p>(<b>a</b>) Actual measured authentication delay in experiments 3 and 4 in AD1, AD2 and in the controlled radio environment; (<b>b</b>) Measured authentication delay as shown in (<b>a</b>) with compensation for internet backhaul latency.</p>
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<p>Probability of successful sensor authentication with mobile gateways traveling at higher speeds based on authentication latency.</p>
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22883 KiB  
Article
A Multi-Stage Method for Connecting Participatory Sensing and Noise Simulations
by Mingyuan Hu, Weitao Che, Qiuju Zhang, Qingli Luo and Hui Lin
Sensors 2015, 15(2), 2265-2282; https://doi.org/10.3390/s150202265 - 22 Jan 2015
Cited by 8 | Viewed by 6151
Abstract
Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale [...] Read more.
Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale monitoring of noise sources, and are updated infrequently. With the emergence of smart cities and ubiquitous sensing, the possible improvements enabled by sensing technologies provide the possibility to resolve this problem. This study proposed an integrated methodology to propel participatory sensing from its current random and distributed sampling origins to professional noise simulation. The aims of this study were to effectively organize the participatory noise data, to dynamically refine the granularity of the noise features on road segments (e.g., different portions of a road segment), and then to provide a reasonable spatio-temporal data foundation to support noise simulations, which can be of help to researchers in understanding how participatory sensing can play a role in smart cities. This study first discusses the potential limitations of the current participatory sensing and simulation-based official noise maps. Next, we explain how participatory noise data can contribute to a simulation-based noise map by providing (1) spatial matching of the participatory noise data to the virtual partitions at a more microscopic level of road networks; (2) multi-temporal scale noise estimations at the spatial level of virtual partitions; and (3) dynamic aggregation of virtual partitions by comparing the noise values at the relevant temporal scale to form a dynamic segmentation of each road segment to support multiple spatio-temporal noise simulations. In this case study, we demonstrate how this method could play a significant role in a simulation-based noise map. Together, these results demonstrate the potential benefits of participatory noise data as dynamic input sources for noise simulations on multiple spatio-temporal scales. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Framework of the multi-stage method.</p>
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<p>Spatial positioning components: geometric representation, semantic road networks, and spatial matching.</p>
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<p>The process of dynamic estimation of noise levels at multi-temporal scales.</p>
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<p>Participatory noise information stream for the main road networks collected by volunteers via the CUHK noise server.</p>
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<p>An example noise estimation for virtual partitions on road segment 15 in two periods of interest: one minute and one hour.</p>
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<p>Demonstration of dynamic partitioning of the road segment on two different timescales.</p>
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<p>Comparison between the measured and estimated noise levels for each virtual partition at eight fixed locations.</p>
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<p>Noise simulation (<b>a</b>) based on average traffic volume and (<b>b</b>) based on the participatory noise data during a time period of one hour.</p>
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<p>Simulation results for the noise distribution in a local area of the CUHK campus. (<b>a</b>) One day and (<b>b</b>) one hour.</p>
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2258 KiB  
Article
Integrated Semantics Service Platform for the Internet of Things: A Case Study of a Smart Office
by Minwoo Ryu, Jaeho Kim and Jaeseok Yun
Sensors 2015, 15(1), 2137-2160; https://doi.org/10.3390/s150102137 - 19 Jan 2015
Cited by 66 | Viewed by 13745
Abstract
The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic [...] Read more.
The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Interoperability between various service domains in a smart city.</p>
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<p>A schema of the IoT-based service integration ontology (IIO) to support ontologies created from various IoT-based service domains. The rectangle represents classes. The solid line represents a relationship between individuals, and the dashed line represents a relationship between super-class and subclasses.</p>
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<p>The overview of the integrated semantic service server (ISSS).</p>
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<p>System architecture of the ISSP.</p>
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<p>A snapshot of the web-based authoring tool.</p>
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<p>Hierarchy of classes, object properties, and data properties of the IIO: (from left), class hierarchy, object properties hierarchy, and data properties hierarchy</p>
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<p>The service flow for the prototype service for the smart office.</p>
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<p>Classification of the space according to the characteristics.</p>
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<p>Hierarchy of classes and object properties.</p>
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1706 KiB  
Article
A Proxy Design to Leverage the Interconnection of CoAP Wireless Sensor Networks with Web Applications
by Alessandro Ludovici and Anna Calveras
Sensors 2015, 15(1), 1217-1244; https://doi.org/10.3390/s150101217 - 9 Jan 2015
Cited by 31 | Viewed by 9687
Abstract
In this paper, we present the design of a Constrained Application Protocol (CoAP) proxy able to interconnect Web applications based on Hypertext Transfer Protocol (HTTP) and WebSocket with CoAP based Wireless Sensor Networks. Sensor networks are commonly used to monitor and control physical [...] Read more.
In this paper, we present the design of a Constrained Application Protocol (CoAP) proxy able to interconnect Web applications based on Hypertext Transfer Protocol (HTTP) and WebSocket with CoAP based Wireless Sensor Networks. Sensor networks are commonly used to monitor and control physical objects or environments. Smart Cities represent applications of such a nature. Wireless Sensor Networks gather data from their surroundings and send them to a remote application. This data flow may be short or long lived. The traditional HTTP long-polling used by Web applications may not be adequate in long-term communications. To overcome this problem, we include the WebSocket protocol in the design of the CoAP proxy. We evaluate the performance of the CoAP proxy in terms of latency and memory consumption. The tests consider long and short-lived communications. In both cases, we evaluate the performance obtained by the CoAP proxy according to the use of WebSocket and HTTP long-polling. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Protocol Stack. The Constrained Application Protocol (CoAP) proxy allows for adapting the protocol stacks of Web applications and CoAP devices.</p>
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<p>Network architecture. The CoAP proxy also has the IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) edge router and gateway functions to interconnect disjointed CoAP networks.</p>
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<p>CoAP and HTTP access and actuation in a Smart City environment.</p>
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<p>Observe protocol. The observer deletes its registration after receiving two updates.</p>
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<p>WebSocket protocol. The WebSocket communication consists of an opening handshake, a data transfer and a closing handshake.</p>
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<p>URI format.</p>
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<p>Translation of the HTTP URI into a CoAP one. The URI used by WebSocket has the same format of the HTTP URI except for the scheme. WebSocket used the “ws://” scheme.</p>
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<p>CoAP proxy design. The CoAP proxy is composed by three modules.</p>
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<p>RD structure. The RD is designed as a tree-structure and it is indexed by the node description.</p>
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1454 KiB  
Article
A Smart City Application: A Fully Controlled Street Lighting Isle Based on Raspberry-Pi Card, a ZigBee Sensor Network and WiMAX
by Fabio Leccese, Marco Cagnetti and Daniele Trinca
Sensors 2014, 14(12), 24408-24424; https://doi.org/10.3390/s141224408 - 18 Dec 2014
Cited by 147 | Viewed by 18250
Abstract
A smart city application has been realized and tested. It is a fully remote controlled isle of lamp posts based on new technologies. It has been designed and organized in different hierarchical layers, which perform local activities to physically control the lamp posts [...] Read more.
A smart city application has been realized and tested. It is a fully remote controlled isle of lamp posts based on new technologies. It has been designed and organized in different hierarchical layers, which perform local activities to physically control the lamp posts and transmit information with another for remote control. Locally, each lamp post uses an electronic card for management and a ZigBee tlc network transmits data to a central control unit, which manages the whole isle. The central unit is realized with a Raspberry-Pi control card due to its good computing performance at very low price. Finally, a WiMAX connection was tested and used to remotely control the smart grid, thus overcoming the distance limitations of commercial Wi-Fi networks. The isle has been realized and tested for some months in the field. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Block scheme of the system architecture.</p>
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<p>Schematic image of the on street system.</p>
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<p>Management strategy of the Smart Grid.</p>
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<p>Lamp control system GUI and measurement of power consumption.</p>
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<p>The system placed in a shelter for laboratory tests.</p>
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<p>Test system in the field.</p>
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458 KiB  
Article
Definition of an Ontology Matching Algorithm for Context Integration in Smart Cities
by Lorena Otero-Cerdeira, Francisco J. Rodríguez-Martínez and Alma Gómez-Rodríguez
Sensors 2014, 14(12), 23581-23619; https://doi.org/10.3390/s141223581 - 8 Dec 2014
Cited by 23 | Viewed by 8297
Abstract
In this paper we describe a novel proposal in the field of smart cities: using an ontology matching algorithm to guarantee the automatic information exchange between the agents and the smart city. A smart city is composed by different types of agents that [...] Read more.
In this paper we describe a novel proposal in the field of smart cities: using an ontology matching algorithm to guarantee the automatic information exchange between the agents and the smart city. A smart city is composed by different types of agents that behave as producers and/or consumers of the information in the smart city. In our proposal, the data from the context is obtained by sensor and device agents while users interact with the smart city by means of user or system agents. The knowledge of each agent, as well as the smart city’s knowledge, is semantically represented using different ontologies. To have an open city, that is fully accessible to any agent and therefore to provide enhanced services to the users, there is the need to ensure a seamless communication between agents and the city, regardless of their inner knowledge representations, i.e., ontologies. To meet this goal we use ontology matching techniques, specifically we have defined a new ontology matching algorithm called OntoPhil to be deployed within a smart city, which has never been done before. OntoPhil was tested on the benchmarks provided by the well known evaluation initiative, Ontology Alignment Evaluation Initiative, and also compared to other matching algorithms, although these algorithms were not specifically designed for smart cities. Additionally, specific tests involving a smart city’s ontology and different types of agents were conducted to validate the usefulness of OntoPhil in the smart city environment. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>Snippet of the smart city's ontology.</p>
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<p>Schematic diagram of algorithm steps.</p>
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<p>Snippet of <span class="html-italic">sensor-observation</span> agent.</p>
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3410 KiB  
Article
A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation
by Zhanlin Ji, Ivan Ganchev, Máirtín O'Droma, Li Zhao and Xueji Zhang
Sensors 2014, 14(12), 22372-22393; https://doi.org/10.3390/s141222372 - 25 Nov 2014
Cited by 159 | Viewed by 29698
Abstract
This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for [...] Read more.
This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide ‘best’ car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>A high-level view of the Internet of Things.</p>
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<p>The IoT platform types.</p>
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<p>A high-level view of an IoT-based Smart City.</p>
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<p>An IoT intelligent car parking system for a Smart City.</p>
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<p>The intelligent car parking services' operational platform.</p>
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<p>An infoStation-based university car parking system.</p>
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<p>A high-level view of a cloud-based car parking system's application layer.</p>
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<p>The main components of the cloud-based car parking system.</p>
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<p>The cloud solutions for the car parking service.</p>
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1439 KiB  
Article
A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps
by Shouyi Yin, Xu Dai, Peng Ouyang, Leibo Liu and Shaojun Wei
Sensors 2014, 14(10), 19561-19581; https://doi.org/10.3390/s141019561 - 20 Oct 2014
Cited by 11 | Viewed by 7217
Abstract
In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, [...] Read more.
In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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<p>The framework of the whole system.</p>
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<p>The feature decomposition operation.</p>
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<p>The layering and label coding step.</p>
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<p>Different situations with the same D value.</p>
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<p>The process of extracting CLDP-D feature. (<b>a</b>) A depth map displayed as an intensity image; (<b>b</b>) A depth map after preprocessing displayed as an intensity image; (<b>c</b>) 3D face rendered as a smooth shaded surface.</p>
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<p>The whole framework of our multi-modal 2D + 3D face recognition method.</p>
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<p>The faces of one subject from fa, fb, fc and dupI subsets (left to right) in turn.</p>
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<p>The chosen 18 images of person 01 for gallery data.</p>
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<p>Results of changing the weights of CLDP-G and CLDP-D.</p>
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Review

Jump to: Research

933 KiB  
Review
Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour—Is It a Piece of Pie?
by Stefan Poslad, Athen Ma, Zhenchen Wang and Haibo Mei
Sensors 2015, 15(6), 13069-13096; https://doi.org/10.3390/s150613069 - 4 Jun 2015
Cited by 83 | Viewed by 12013
Abstract
Whilst there is an increasing capability to instrument smart cities using fixed and mobile sensors to produce the big data to better understand and manage transportation use, there still exists a wide gap between the sustainability goals of smart cities, e.g., to promote [...] Read more.
Whilst there is an increasing capability to instrument smart cities using fixed and mobile sensors to produce the big data to better understand and manage transportation use, there still exists a wide gap between the sustainability goals of smart cities, e.g., to promote less private car use at peak times, with respect to their ability to more dynamically support individualised shifts in multi-modal transportation use to help achieve such goals. We describe the development of the tripzoom system developed as part of the SUNSET—SUstainable social Network SErvices for Transport—project to research and develop a mobile and fixed traffic sensor system to help facilitate individual mobility shifts. Its main novelty was its ability to use mobile sensors to classify common multiple urban transportation modes, to generate information-rich individual and group mobility profiles and to couple this with the use of a targeted incentivised marketplace to gamify travel. This helps to promote mobility shifts towards achieving sustainability goals. This system was trialled in three European country cities operated as Living Labs over six months. Our main findings were that we were able to accomplish a level of behavioural shifts in travel behaviour. Hence, we have provided a proof-of-concept system that uses positive incentives to change individual travel behaviour. Full article
(This article belongs to the Special Issue Sensors and Smart Cities)
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Figure 1

Figure 1
<p>High-level architecture for the tripzoom system.</p>
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<p>System interaction to support incentives distribution (the database symbol at the edge of some components indicates that that component is associated with an internal data store; T identifies the part of the project, the task, that developed that component).</p>
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<p>Incentive Data Model.</p>
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<p>Distributing challenges and rewards to the Android tripzoom app.</p>
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<p>Structure of a “Target and Challenge” Incentive.</p>
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<p>Incentive registration.</p>
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<p>Challenges and Rewards.</p>
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<p>SUNSET deployment outline.</p>
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<p>Timeline of experiments in Enschede, Gothenburg and Leeds.</p>
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