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New Paradigms in Cyber-Physical Social Sensing
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New Paradigms in Cyber-Physical Social Sensing

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

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 110256

Special Issue Editors

Dr. Mianxiong Dong

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Guest Editor
Department of Sciences and Informatics, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
Interests: wireless networks; cloud computing; cyberphysical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhi Liu

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Guest Editor
Global Information and Telecommunication Institute (GITI), Waseda University, Tokyo, Japan
Interests: wireless networks; video/image processing and transmission
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anfeng Liu

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Guest Editor
School of Information Science and Engineering, Central South University, Changsha 410083, China
Interests: wireless sensor networks; network security, trust and privacy; green data collection; Internet of Things; mobile crowdsourcing; routing protocols
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Didier El Baz

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Guest Editor
Distributed Computing and Asynchronism Team (CDA), LAAS-CNRS, Toulouse, France
Interests: applied mathematics and parallel and distributed computing; heterogeneous computing and peer-to-peer computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The goal of Cyber Physical Social Sensing (CPSS) is to form a ubiquitous mobile wireless sensor network using intelligent terminals equipped with various sensors and perceive human social information including the environment, transportation, social activities, etc. It wirelessly uploads the information to the server, and then composites the information and provides the user with a higher level of combined information or services. CPSS enriches human-to-human, human-to-object, and object-to-object interactions in the physical world, human society, as well as in the virtual world. Furthermore, it allows people to participate in the perception process through the mobile terminals and provides pervasive service for people. CPSS expands the dimensions of human perception of the world, and changes the way that people perceive the world. Additionally, it will exhibit a variety of complicated characteristics, which provides more open issues and challenges for research communities.

The goal of this Special Issue is to seek original articles examining the state-of-the-art, open challenging research issues, new research results, and solutions in Cyber-Physical Social Sensing. All submissions should contain substantial tutorial content and be accessible to a general audience of researchers and practitioners.

Prof. Dr. Mianxiong Dong
Prof. Dr. Zhi Liu
Prof. Dr. Anfeng Liu
Prof. Dr. Didier El Baz
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Architecture design of perception system
  • Protocol design of in CPSS
  • Localization, node mobility model for CPSS
  • Construction technology of dynamics group in CPSS
  • Methods for data collection, convergence and storage in CPSS
  • Schemes of data mining, processing and analysis in CPSS
  • Audit mechanism and verification mechanism for data credibility verification in CPSS
  • Data visualization in CPSS
  • Security, as well as privacy performance modeling, analysis, and optimization for CPSS
  • Quality of Experience and Quality of Service in CPSS
  • Economics and pricing mechanism for CPSS
  • Application of social perception calculation and other related applications in CPSS
  • Experimental platform design for CPSS

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Published Papers (20 papers)

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Research

6065 KiB  
Article
A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
by Jinhuan Zhang, Jun Long, Chengyuan Zhang and Guihu Zhao
Sensors 2017, 17(2), 395; https://doi.org/10.3390/s17020395 - 17 Feb 2017
Cited by 11 | Viewed by 3937
Abstract
Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme [...] Read more.
Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme named TSR-DARDA for spherical physical objects is proposed. By dividing the spherical network constructed by dispersed sensor nodes into circular tiers with specifically designed widths and cells, TSTR-DARDA tries to enable as many nodes as possible to transmit data simultaneously. In order to ensure transmission reliability, lost packets are retransmitted. Moreover, to minimize the latency while maintaining reliability for data collection, in-network aggregation and broadcast techniques are adopted to deal with the transmission between data collecting nodes in the outer layer and their parent data collecting nodes in the inner layer. Thus, the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters. To demonstrate the effectiveness of the proposed scheme, we have conducted extensive theoretical analysis and comparisons to evaluate the performance of TSR-DARDA. The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>Sensor nodes deployed to collect information for a TV tower.</p> Full article ">Figure 2
<p>The envisioned spherical network with sensors deployed in physical objects.</p> Full article ">Figure 3
<p>The overall approach.</p> Full article ">Figure 4
<p>Projection from the three-dimensional space to two-dimensional plane of tiers and cells partition.</p> Full article ">Figure 5
<p>Illustration of parameters relationship for tiers and cells partition.</p> Full article ">Figure 6
<p>Implementation of the proposed TSR-DARDA scheme.</p> Full article ">Figure 7
<p>Optimization of the proposed TSR-DARDA scheme.</p> Full article ">Figure 8
<p>Network topology with nodes randomly scattered in half sphere surface. (<b>a</b>) 100 nodes; (<b>b</b>) 500 nodes.</p> Full article ">Figure 9
<p>Comparisons of the number of packets lost under different <span class="html-italic">p</span> and <span class="html-italic">μ</span> for a network with 100 nodes.</p> Full article ">Figure 10
<p>Comparisons of network delay under different <span class="html-italic">p</span> and <span class="html-italic">μ</span> for a network with 100 nodes.</p> Full article ">Figure 11
<p>Comparisons of the number of packets lost under different <span class="html-italic">p</span> and <span class="html-italic">μ</span> for a network with 500 nodes.</p> Full article ">Figure 12
<p>Comparisons of network delay under different <span class="html-italic">p</span> and <span class="html-italic">μ</span> for a network with 500 nodes.</p> Full article ">Figure 13
<p>Comparison under different <span class="html-italic">μ</span> for a network with 100 nodes when <span class="html-italic">p</span> = 0.6. (<b>a</b>) The network delay; (<b>b</b>) The number of packets lost.</p> Full article ">Figure 14
<p>Comparison under different <span class="html-italic">μ</span> for a network with 100 nodes when <span class="html-italic">p</span> = 0.8. (<b>a</b>) The network delay; (<b>b</b>) The number of packets lost.</p> Full article ">Figure 15
<p>Comparison under different <span class="html-italic">μ</span> for a network with 500 nodes when <span class="html-italic">p</span> = 0.6. (<b>a</b>) The network delay; (<b>b</b>) The number of packets lost.</p> Full article ">Figure 16
<p>Comparison under different <span class="html-italic">μ</span> for a network with 500 nodes when <span class="html-italic">p</span> = 0.8. (<b>a</b>) The network delay; (<b>b</b>) The number of packets lost.</p> Full article ">
727 KiB  
Article
Smooth Sensor Motion Planning for Robotic Cyber Physical Social Sensing (CPSS)
by Hong Tang, Liangzhi Li and Nanfeng Xiao
Sensors 2017, 17(2), 393; https://doi.org/10.3390/s17020393 - 17 Feb 2017
Cited by 5 | Viewed by 4718
Abstract
Although many researchers have begun to study the area of Cyber Physical Social Sensing (CPSS), few are focused on robotic sensors. We successfully utilize robots in CPSS, and propose a sensor trajectory planning method in this paper. Trajectory planning is a fundamental problem [...] Read more.
Although many researchers have begun to study the area of Cyber Physical Social Sensing (CPSS), few are focused on robotic sensors. We successfully utilize robots in CPSS, and propose a sensor trajectory planning method in this paper. Trajectory planning is a fundamental problem in mobile robotics. However, traditional methods are not suited for robotic sensors, because of their low efficiency, instability, and non-smooth-generated paths. This paper adopts an optimizing function to generate several intermediate points and regress these discrete points to a quintic polynomial which can output a smooth trajectory for the robotic sensor. Simulations demonstrate that our approach is robust and efficient, and can be well applied in the CPSS field. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>Mobile robotic sensing for Cyber Physical Social Sensing (CPSS).</p> Full article ">Figure 2
<p>Schematic of the proposed trajectory planning method.</p> Full article ">Figure 3
<p>Visual sensor and binocular positioning.</p> Full article ">Figure 4
<p>Binocular stereo sensor.</p> Full article ">Figure 5
<p>Schematic of the proposed method.</p> Full article ">Figure 6
<p>Robotic kinematic model of UR5.</p> Full article ">Figure 7
<p>UR5 robot used in the experiments.</p> Full article ">Figure 8
<p>Robot state. (<b>a</b>) The initial and terminate state; (<b>b</b>) The state from another perspective.</p> Full article ">Figure 9
<p>The angle variation of each joint.</p> Full article ">Figure 10
<p>The angular velocity. (<b>a</b>) 1st joint; (<b>b</b>) 2nd joint; (<b>c</b>) 3rd joint; (<b>d</b>) 4th joint; (<b>e</b>) 5th joint.</p> Full article ">Figure 11
<p>The acceleration curve. (<b>a</b>) 1st joint; (<b>b</b>) 2nd joint; (<b>c</b>) 3rd joint; (<b>d</b>) 4th joint; (<b>e</b>) 5th joint.</p> Full article ">Figure 12
<p>Corresponding trajectories. (<b>a</b>) 2nd joint; (<b>b</b>) 3rd joint; (<b>c</b>) 4th joint; (<b>d</b>) 5th joint.</p> Full article ">
735 KiB  
Article
Capacity-Delay Trade-Off in Collaborative Hybrid Ad-Hoc Networks with Coverage Sensing
by Lingyu Chen, Wenbin Luo, Chen Liu, Xuemin Hong and Jianghong Shi
Sensors 2017, 17(2), 232; https://doi.org/10.3390/s17020232 - 26 Jan 2017
Cited by 4 | Viewed by 4938
Abstract
The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied [...] Read more.
The integration of ad hoc device-to-device (D2D) communications and open-access small cells can result in a networking paradigm called hybrid the ad hoc network, which is particularly promising in delivering delay-tolerant data. The capacity-delay performance of hybrid ad hoc networks has been studied extensively under a popular framework called scaling law analysis. These studies, however, do not take into account aspects of interference accumulation and queueing delay and, therefore, may lead to over-optimistic results. Moreover, focusing on the average measures, existing works fail to give finer-grained insights into the distribution of delays. This paper proposes an alternative analytical framework based on queueing theoretic models and physical interference models. We apply this framework to study the capacity-delay performance of a collaborative cellular D2D network with coverage sensing and two-hop relay. The new framework allows us to fully characterize the delay distribution in the transform domain and pinpoint the impacts of coverage sensing, user and base station densities, transmit power, user mobility and packet size on the capacity-delay trade-off. We show that under the condition of queueing equilibrium, the maximum throughput capacity per device saturates to an upper bound of 0.7239 λ b / λ u bits/s/Hz, where λ b and λ u are the densities of base stations and mobile users, respectively. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>System model of the hybrid ad hoc network with user collaboration and coverage sensing.</p> Full article ">Figure 2
<p>Queueing model representation of the hybrid ad hoc network.</p> Full article ">Figure 3
<p>Relationships among system parameters, protocol parameters and queueing parameters.</p> Full article ">Figure 4
<p>Coverage sensing area of a mobile user.</p> Full article ">Figure 5
<p>Maximum capacity per device as a function of transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>II</mi> </msub> </semantics> </math> with varying infrastructure density <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> (<math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>b</mi> </msub> <mo>/</mo> <msub> <mi>λ</mi> <mi>u</mi> </msub> </mrow> </semantics> </math> =1).</p> Full article ">Figure 6
<p>CDF of waiting time <math display="inline"> <semantics> <msub> <mi>w</mi> <mi>II</mi> </msub> </semantics> </math> with varying coverage outage fraction <math display="inline"> <semantics> <msub> <mi>ε</mi> <mi>o</mi> </msub> </semantics> </math> when the coverage outage duration <math display="inline"> <semantics> <msub> <mi>β</mi> <mi>o</mi> </msub> </semantics> </math> follows the exponential and Gamma distribution (<math display="inline"> <semantics> <msub> <mi>ε</mi> <mi>o</mi> </msub> </semantics> </math> increases from 0.1–0.7 with steps of 0.1, <span class="html-italic">k</span> = 2, <span class="html-italic">N</span> = 1, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mi>o</mi> </msub> </semantics> </math> = 20, <math display="inline"> <semantics> <msub> <mi>ε</mi> <mi>e</mi> </msub> </semantics> </math> = 0.2, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mi>e</mi> </msub> </semantics> </math> = 1).</p> Full article ">Figure 7
<p>CDF of waiting time <math display="inline"> <semantics> <msub> <mi>w</mi> <mi>II</mi> </msub> </semantics> </math> with varying number of collaborating devices <span class="html-italic">N</span> when the service outage duration <math display="inline"> <semantics> <msub> <mi>β</mi> <mi>o</mi> </msub> </semantics> </math> follows the exponential and Gamma distribution (<span class="html-italic">N</span> increases from 1–5 with steps of one, <span class="html-italic">k</span> = 2, <math display="inline"> <semantics> <msub> <mi>ε</mi> <mi>o</mi> </msub> </semantics> </math> = 0.6, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mi>o</mi> </msub> </semantics> </math> = 20, <math display="inline"> <semantics> <msub> <mi>ε</mi> <mi>e</mi> </msub> </semantics> </math> = 0.2, <math display="inline"> <semantics> <msub> <mover accent="true"> <mi>α</mi> <mo stretchy="false">¯</mo> </mover> <mi>e</mi> </msub> </semantics> </math> = 1).</p> Full article ">Figure 8
<p>The average number of collaborating devices <math display="inline"> <semantics> <mover accent="true"> <mi>N</mi> <mo stretchy="false">¯</mo> </mover> </semantics> </math> as a function of broadcast rate <math display="inline"> <semantics> <msub> <mi>R</mi> <mi mathvariant="normal">I</mi> </msub> </semantics> </math> with varying transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">I</mi> </msub> </semantics> </math> and capacity demand <span class="html-italic">C</span> (<math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>u</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </semantics> </math>).</p> Full article ">Figure 9
<p>The mean waiting time <math display="inline"> <semantics> <msubsup> <mover accent="true"> <mi>w</mi> <mo stretchy="false">¯</mo> </mover> <mi>II</mi> <mn>1</mn> </msubsup> </semantics> </math> as a function of average delivery rate <math display="inline"> <semantics> <msub> <mi>R</mi> <mi>II</mi> </msub> </semantics> </math> with varying transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi>II</mi> </msub> </semantics> </math> and capacity demand <span class="html-italic">C</span> (<math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </semantics> </math>, <span class="html-italic">L</span> = 1, <span class="html-italic">v</span> = 1).</p> Full article ">Figure 10
<p>Mean delay <math display="inline"> <semantics> <mover accent="true"> <mi>D</mi> <mo stretchy="false">¯</mo> </mover> </semantics> </math> as a function of capacity per device <span class="html-italic">C</span> with varying transmit power <math display="inline"> <semantics> <msub> <mi>P</mi> <mi mathvariant="normal">I</mi> </msub> </semantics> </math> and user density <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>u</mi> </msub> </semantics> </math> (<span class="html-italic">L</span> = 1, <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>II</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> </semantics> </math>, <span class="html-italic">v</span> = 1).</p> Full article ">Figure 11
<p>Mean delay <math display="inline"> <semantics> <mover accent="true"> <mi>D</mi> <mo stretchy="false">¯</mo> </mover> </semantics> </math> as a function of capacity per device <span class="html-italic">C</span> with varying user mobile speed <span class="html-italic">v</span> (<span class="html-italic">L</span> = 1, <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">I</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>II</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> </semantics> </math>, arbitrary <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>u</mi> </msub> </semantics> </math>).</p> Full article ">Figure 12
<p>Mean delay <math display="inline"> <semantics> <mover accent="true"> <mi>D</mi> <mo stretchy="false">¯</mo> </mover> </semantics> </math> as a function of capacity per device <span class="html-italic">C</span> with varying packet size <span class="html-italic">L</span> (<math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">I</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>II</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> </semantics> </math>, arbitrary <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>u</mi> </msub> </semantics> </math>, <span class="html-italic">v</span> = 1).</p> Full article ">Figure 13
<p>Outage delay <math display="inline"> <semantics> <mrow> <mi>D</mi> <mo>(</mo> <mi>ϕ</mi> <mo>)</mo> </mrow> </semantics> </math> as a function of capacity per device <span class="html-italic">C</span> with varying outage threshold <math display="inline"> <semantics> <mrow> <mi>ϕ</mi> </mrow> </semantics> </math> (<math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">I</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>II</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>b</mi> </msub> </semantics> </math> = <math display="inline"> <semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> </semantics> </math>, arbitrary <math display="inline"> <semantics> <msub> <mi>λ</mi> <mi>u</mi> </msub> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <span class="html-italic">v</span> = 1).</p> Full article ">
590 KiB  
Article
Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems
by Shuqiang Huang and Ming Tao
Sensors 2017, 17(1), 209; https://doi.org/10.3390/s17010209 - 22 Jan 2017
Cited by 7 | Viewed by 5649
Abstract
Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to [...] Read more.
Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>WMN gateway deployment using CPS.</p> Full article ">Figure 2
<p>Gateway deployment of a 7-node network. (<b>A</b>) original network; (<b>B</b>) gateway deploying on node; (<b>C</b>) gateway deploying not on node.</p> Full article ">Figure 3
<p>Deployment diagram of two gateways. (<b>A</b>) gateways deploying on nodes; (<b>B</b>) gateways deploying not on nodes.</p> Full article ">Figure 4
<p>Search mechanism of the Competitive Swarm Optimizer (CSO) algorithm.</p> Full article ">Figure 5
<p>Gateway deployment of a 7-node network. (<b>A</b>) 50 nodes; (<b>B</b>) 200 nodes; (<b>C</b>) 600 nodes; (<b>D</b>) 1000 nodes.</p> Full article ">Figure 6
<p>Convergence of three algorithms at different network scales. (<b>A</b>) 50 nodes; (<b>B</b>) 200 nodes; (<b>C</b>) 600 nodes; (<b>D</b>) 1000 nodes.</p> Full article ">Figure 7
<p>Optimization of three algorithms for networks of different sizes.</p> Full article ">Figure 8
<p>Number of hops of three types of algorithms at different network scales.</p> Full article ">
3330 KiB  
Article
A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments
by Yunjian Jia, Zhenyu Zhou, Fei Chen, Peng Duan, Zhen Guo and Shahid Mumtaz
Sensors 2017, 17(1), 143; https://doi.org/10.3390/s17010143 - 13 Jan 2017
Cited by 7 | Viewed by 5311
Abstract
Tracking people’s behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless [...] Read more.
Tracking people’s behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people’s access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people’s access behaviors can be correctly tracked within a one-second delay. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Description of formulating people’s access behaviors in Wi-Fi networks.</p> Full article ">Figure 2
<p>Overall 802.11 state diagram.</p> Full article ">Figure 3
<p>Procedures of accessing an encrypted AP. (<b>a</b>) Access procedures; (<b>b</b>) The 4-way handshake.</p> Full article ">Figure 4
<p>Overview of system architecture.</p> Full article ">Figure 5
<p>Generic 802.11 frame format.</p> Full article ">Figure 6
<p>Format of the QoS Data frame with the EAPOL-Key message.</p> Full article ">Figure 7
<p>Experimental setup: (<b>a</b>) Experiment equipment; (<b>b</b>) The GUI of the monitor; (<b>c</b>) The floor plan of the experiment.</p> Full article ">Figure 8
<p>Experimental results: (<b>a</b>) Records of the experiment; (<b>b</b>) Output information of the system.</p> Full article ">Figure 9
<p>Frame type and transmission direction.</p> Full article ">Figure 10
<p>Status codes and reason codes.</p> Full article ">Figure 11
<p><span class="html-italic">ValiFlag</span> value for the access operation of each device.</p> Full article ">
569 KiB  
Article
Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme
by Yang Xu, Xiong Luo, Weiping Wang and Wenbing Zhao
Sensors 2017, 17(1), 135; https://doi.org/10.3390/s17010135 - 12 Jan 2017
Cited by 51 | Viewed by 6765
Abstract
Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor [...] Read more.
Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>A diagram of the range measurement error for the distance vector hop (DV-HOP) algorithm.</p> Full article ">Figure 2
<p>Different areas of node distribution. (<b>a</b>) There are 50 nodes in the area of 50 m × 50 m; (<b>b</b>) There are 100 nodes in the area of 100 m × 100 m.</p> Full article ">Figure 3
<p>The impact of <span class="html-italic">R</span> in different areas. (<b>a</b>) The location error against <span class="html-italic">R</span> in the area of 50 m × 50 m; (<b>b</b>) The location error against <span class="html-italic">R</span> in the area of 100 m × 100 m.</p> Full article ">Figure 4
<p>The impact of <span class="html-italic">ε</span> in different areas when <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics> </math>. (<b>a</b>) The average <span class="html-italic">ε</span> in the area of 50 m × 50 m when <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics> </math>; (<b>b</b>) The average <span class="html-italic">ε</span> in the area of 100 m × 100 m when <math display="inline"> <semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Localization errors against the amount of anchor nodes. (<b>a</b>) The case with 50 nodes in the area of 50 m × 50 m; (<b>b</b>) The case with 100 nodes in the area of 100 m × 100 m.</p> Full article ">Figure 6
<p>Localization errors against the amount of RSSI samples. (<b>a</b>) The case with 50 nodes in the area of 50 m × 50 m; (<b>b</b>) The case with 100 nodes in the area of 100 m × 100 m.</p> Full article ">Figure 7
<p>Localization errors against the noise standard deviation. (<b>a</b>) The case with 50 nodes in the area of 50 m × 50 m; (<b>b</b>) The case with 100 nodes in the area of 100 m × 100 m.</p> Full article ">Figure 8
<p>Localization errors against the outliers. (<b>a</b>) The case with 50 nodes in the area of 50 m × 50 m; (<b>b</b>) The case with 100 nodes in the area of 100 m × 100 m.</p> Full article ">
12468 KiB  
Article
Spectrum Sharing Based on a Bertrand Game in Cognitive Radio Sensor Networks
by Biqing Zeng, Chi Zhang, Pianpian Hu and Shengyu Wang
Sensors 2017, 17(1), 101; https://doi.org/10.3390/s17010101 - 7 Jan 2017
Cited by 11 | Viewed by 5357
Abstract
In the study of power control and allocation based on pricing, the utility of secondary users is usually studied from the perspective of the signal to noise ratio. The study of secondary user utility from the perspective of communication demand can not only [...] Read more.
In the study of power control and allocation based on pricing, the utility of secondary users is usually studied from the perspective of the signal to noise ratio. The study of secondary user utility from the perspective of communication demand can not only promote the secondary users to meet the maximum communication needs, but also to maximize the utilization of spectrum resources, however, research in this area is lacking, so from the viewpoint of meeting the demand of network communication, this paper designs a two stage model to solve spectrum leasing and allocation problem in cognitive radio sensor networks (CRSNs). In the first stage, the secondary base station collects the secondary network communication requirements, and rents spectrum resources from several primary base stations using the Bertrand game to model the transaction behavior of the primary base station and secondary base station. The second stage, the subcarriers and power allocation problem of secondary base stations is defined as a nonlinear programming problem to be solved based on Nash bargaining. The simulation results show that the proposed model can satisfy the communication requirements of each user in a fair and efficient way compared to other spectrum sharing schemes. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>The model of multi-primary networks and a secondary network.</p> Full article ">Figure 2
<p>The supply and demand curves.</p> Full article ">Figure 3
<p>The relationship between spectrum demand and optimal rent number.</p> Full article ">Figure 4
<p>Spectrum pricing and allocation scheduling.</p> Full article ">Figure 5
<p>Spectrum pricing and allocation scheduling.</p> Full article ">Figure 6
<p>Influence of preference coefficient on spectrum demand.</p> Full article ">Figure 7
<p>Utility function of a primary base station.</p> Full article ">Figure 8
<p>Optimal Response Function and Nash Equilibrium.</p> Full article ">Figure 9
<p>Dynamic game and Nash Equilibrium.</p> Full article ">Figure 10
<p>The relationship between the update step size and the number of iterations.</p> Full article ">Figure 11
<p>Maximum revenue of the primary base station.</p> Full article ">Figure 12
<p>Secondary base station spectrum optimal rent number.</p> Full article ">Figure 13
<p>Spectrum demand and spectrum rent.</p> Full article ">Figure 14
<p>Each secondary user channel capacity.</p> Full article ">Figure 15
<p>The optimal time assign of secondary users.</p> Full article ">Figure 16
<p>Spectrum requirements for secondary network of two cases.</p> Full article ">Figure 17
<p>Each secondary user’s channel capacity.</p> Full article ">Figure 18
<p>The optimal time assignments of secondary users.</p> Full article ">Figure 19
<p>The cost of two secondary network leased spectrum cases.</p> Full article ">Figure 20
<p>Secondary user channel capacity comparison I.</p> Full article ">Figure 21
<p>Secondary user channel capacity comparison II.</p> Full article ">Figure 22
<p>The comparison of total throughput for the secondary network I.</p> Full article ">Figure 23
<p>The comparison of total throughput for the secondary network II.</p> Full article ">Figure 24
<p>The comparison of fairness I.</p> Full article ">Figure 25
<p>The comparison of fairness II.</p> Full article ">
4479 KiB  
Article
Enhance the Quality of Crowdsensing for Fine-Grained Urban Environment Monitoring via Data Correlation
by Xu Kang, Liang Liu and Huadong Ma
Sensors 2017, 17(1), 88; https://doi.org/10.3390/s17010088 - 4 Jan 2017
Cited by 11 | Viewed by 5159
Abstract
Monitoring the status of urban environments, which provides fundamental information for a city, yields crucial insights into various fields of urban research. Recently, with the popularity of smartphones and vehicles equipped with onboard sensors, a people-centric scheme, namely “crowdsensing”, for city-scale environment monitoring [...] Read more.
Monitoring the status of urban environments, which provides fundamental information for a city, yields crucial insights into various fields of urban research. Recently, with the popularity of smartphones and vehicles equipped with onboard sensors, a people-centric scheme, namely “crowdsensing”, for city-scale environment monitoring is emerging. This paper proposes a data correlation based crowdsensing approach for fine-grained urban environment monitoring. To demonstrate urban status, we generate sensing images via crowdsensing network, and then enhance the quality of sensing images via data correlation. Specifically, to achieve a higher quality of sensing images, we not only utilize temporal correlation of mobile sensing nodes but also fuse the sensory data with correlated environment data by introducing a collective tensor decomposition approach. Finally, we conduct a series of numerical simulations and a real dataset based case study. The results validate that our approach outperforms the traditional spatial interpolation-based method. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Crowdsensing networks based urban environment monitoring. (<b>a</b>) The crowdsensing networks, consisting of vast smartphones/vehicles, work as an urban camera; (<b>b</b>) Sensing image of target phenomenon generated by the crowdsensing network via interpolation method; (<b>c</b>) Sensing trajectories; (<b>d</b>) Correlated signals; (<b>e</b>) Sensing image after using enhanced crowdsensing approach.</p> Full article ">Figure 2
<p>The process of generating sensing image via crowdsensing. (<b>a</b>) The 2D signal of <math display="inline"> <semantics> <msub> <mrow> <mi>CO</mi> </mrow> <mn>2</mn> </msub> </semantics> </math> concentration; (<b>b</b>) The distribution of 100 nodes in <span class="html-italic">R</span> with their sending time <math display="inline"> <semantics> <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>∈</mo> <mi>T</mi> </mrow> </semantics> </math>; (<b>c</b>) The sampling points of 2D signal; (<b>d</b>) Sensing image generated by <span class="html-italic">V</span>.</p> Full article ">Figure 3
<p>(<b>a</b>) Division of unit square <span class="html-italic">R</span> by <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>×</mo> <mi>m</mi> </mrow> </semantics> </math> uniform grids; (<b>b</b>) A voronoi diagram based on the locations of crowdsensing nodes.</p> Full article ">Figure 4
<p>Collaborative tensor decomposition.</p> Full article ">Figure 5
<p>Correlated time slots combination.</p> Full article ">Figure 6
<p>An instant of 6000 crowdsensing nodes generated by SLAW model.</p> Full article ">Figure 7
<p>Correlated signals. (<b>a</b>) Linear correlated signal: <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> <mfenced separators="" open="(" close=")"> <msub> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mfenced> <mo>=</mo> <mn>0.95</mn> <mo>×</mo> <msub> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>+</mo> <mn>0.05</mn> <mo>+</mo> <mi>N</mi> <mfenced separators="" open="(" close=")"> <mn>0</mn> <mo>,</mo> <mn>0.06</mn> </mfenced> </mrow> </semantics> </math>; (<b>b</b>) Non-linear correlated signal: <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mfenced separators="" open="(" close=")"> <msub> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mfenced> <mo>=</mo> <mn>0.9</mn> <mo>×</mo> <msubsup> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> <mo>+</mo> <mn>0.9</mn> <mo>×</mo> <msub> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>+</mo> <mn>0.1</mn> <mo>+</mo> <mi>N</mi> <mfenced separators="" open="(" close=")"> <mn>0</mn> <mo>,</mo> <mn>0.11</mn> </mfenced> </mrow> </semantics> </math>.</p> Full article ">Figure 8
<p>(<b>a</b>) Distribution of 6000 nodes (Groups I,II, and III ) over the whole <math display="inline"> <semantics> <mrow> <mn>5</mn> <mo>×</mo> <mn>5</mn> </mrow> </semantics> </math> km<math display="inline"> <semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics> </math> region which is divided into 25 unit squares; (<b>b</b>) Distribution of crowdsensing nodes over the unit area indicated by the red box in (<b>a</b>).</p> Full article ">Figure 9
<p>Recovered signals (generated sensing images). (<b>a</b>) Interpolation method; (<b>b</b>) Enhanced crowdsensing approach with 3 time slots combination; (<b>c</b>) Enhanced crowdsensing approach with 6 time slots combination.</p> Full article ">Figure 10
<p>Correlation coefficients <math display="inline"> <semantics> <mrow> <mi>C</mi> <mfenced separators="" open="(" close=")"> <msup> <mi mathvariant="bold">Z</mi> <mrow> <mo>″</mo> </mrow> </msup> <mo>,</mo> <mi mathvariant="bold">Z</mi> </mfenced> </mrow> </semantics> </math> against different values of <span class="html-italic">n</span>.</p> Full article ">Figure 11
<p>Relationship between crowdsensing resolution <span class="html-italic">r</span> and <span class="html-italic">s</span> crowdsensing nodes in an unit area which are generated by SLAW model. The x-axis denotes <math display="inline"> <semantics> <msqrt> <mi>s</mi> </msqrt> </semantics> </math>, and the y-axis denotes <math display="inline"> <semantics> <msub> <mi>n</mi> <mi>l</mi> </msub> </semantics> </math>, i.e., <math display="inline"> <semantics> <msqrt> <mi>r</mi> </msqrt> </semantics> </math>.</p> Full article ">Figure 12
<p>(<b>a</b>) Air quality monitoring stations in Beijing viewed from Google Earth; (<b>b</b>) The selected monitoring area within 2th ring road of Beijing.</p> Full article ">Figure 13
<p>Variation tendency of PM<math display="inline"> <semantics> <mrow> <mn>2.5</mn> </mrow> </semantics> </math> and PM10 in monitoring station <math display="inline"> <semantics> <mrow> <mi>S</mi> <mn>9</mn> </mrow> </semantics> </math> during the time period of 21–27 April 2013.</p> Full article ">Figure 14
<p>Variation tendency of PM<math display="inline"> <semantics> <mrow> <mn>2.5</mn> </mrow> </semantics> </math> and air humidity in monitoring station <math display="inline"> <semantics> <mrow> <mi>S</mi> <mn>9</mn> </mrow> </semantics> </math> during the time period of 21–27 April 2013.</p> Full article ">Figure 15
<p>The constructed signals within the red box zones of <a href="#sensors-17-00088-f012" class="html-fig">Figure 12</a>b. (<b>a</b>) Signal of PM<math display="inline"> <semantics> <mrow> <mn>2.5</mn> </mrow> </semantics> </math>; (<b>b</b>) Signal of PM10; (<b>c</b>) Signal of air humidity.</p> Full article ">Figure 16
<p>Recovered signals (sensing images). (<b>a</b>) The generated sensing image based on interpolation method; (<b>b</b>) The generated sensing image based on enhanced crowdsensing approach.</p> Full article ">
2497 KiB  
Article
Node Immunization with Time-Sensitive Restrictions
by Wen Cui, Xiaoqing Gong, Chen Liu, Dan Xu, Xiaojiang Chen, Dingyi Fang, Shaojie Tang, Fan Wu and Guihai Chen
Sensors 2016, 16(12), 2141; https://doi.org/10.3390/s16122141 - 15 Dec 2016
Cited by 6 | Viewed by 5019
Abstract
When we encounter a malicious rumor or an infectious disease outbreak, immunizing k nodes of the relevant network with limited resources is always treated as an extremely effective method. The key challenge is how we can insulate limited nodes to minimize the propagation [...] Read more.
When we encounter a malicious rumor or an infectious disease outbreak, immunizing k nodes of the relevant network with limited resources is always treated as an extremely effective method. The key challenge is how we can insulate limited nodes to minimize the propagation of those contagious things. In previous works, the best k immunised nodes are selected by learning the initial status of nodes and their strategies even if there is no feedback in the propagation process, which eventually leads to ineffective performance of their solutions. In this paper, we design a novel vaccines placement strategy for protecting much more healthy nodes from being infected by infectious nodes. The main idea of our solution is that we are not only utilizing the status of changing nodes as auxiliary knowledge to adjust our scheme, but also comparing the performance of vaccines in various transmission slots. Thus, our solution has a better chance to get more benefit from these limited vaccines. Extensive experiments have been conducted on several real-world data sets and the results have shown that our algorithm has a better performance than previous works. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Different benefit of position over each placement time.</p> Full article ">Figure 2
<p>Intuition underlying DWPV use of time division in distributing vaccines: The figure shows an infected node (in red) and some susceptible nodes (in blue). The infected node has one chance to infect their neighbors at <span class="html-italic">t</span> = 0. At <span class="html-italic">t</span> = 1, the node A has been infected by the initially infected node and other nodes (B and C) are lucky ones.</p> Full article ">Figure 3
<p>SIR transmission process: At <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>a</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>b</mi> </msub> </semantics> </math> are the initial infected nodes in <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. Those two nodes are trying to infect their neighbors through weighted connection edges. At the same time, both <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>a</mi> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>b</mi> </msub> </semantics> </math> are struggling to become recovered by using their own antibodies, the probability of occurrence of this event is <span class="html-italic">R</span>. At <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, a new infectious node <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics> </math> is coming and trying to infect its neighbors, fortunately, <math display="inline"> <semantics> <msub> <mi>I</mi> <mi>b</mi> </msub> </semantics> </math> overcame the hateful disease and protected its neighbors (which are in the green area) from being infected. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>Fundamental Knowledge of Our Method: At first, some miserable people had caught a malignant virus and it would be spread to more and more people who have connection with those infected men; Then, we translated the abstract of social network into graph <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> for designing an appropriate method to control the malignant virus; At last, we convert <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> into <math display="inline"> <semantics> <mrow> <mi>D</mi> <mi>T</mi> </mrow> </semantics> </math> by combing the infected nodes as one super infected node and let that super infected node to be the root of <math display="inline"> <semantics> <mrow> <mi>D</mi> <mi>T</mi> </mrow> </semantics> </math>. Once we have <math display="inline"> <semantics> <mrow> <mi>D</mi> <mi>T</mi> </mrow> </semantics> </math>, we just need to select out the <span class="html-italic">best-k-benefit</span> nodes in MDL. Finally, we can get the vaccines set <math display="inline"> <semantics> <mi mathvariant="script">V</mi> </semantics> </math> at <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> for the SVP problem. (<b>a</b>) People infected; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math>; (<b>c</b>) Dominating Tree(DT).</p> Full article ">Figure 5
<p>Examples for one vaccine: A simple case like <span class="html-italic">Simple Case 1</span> that we can put the only one vaccine on node A with no doubt. <span class="html-italic">Simple Case 2</span> is also not a hard decision for us. After comparing the benefit between A and B, we can put vaccine on B to obtain a bigger <math display="inline"> <semantics> <msub> <mi mathvariant="script">V</mi> <mi>B</mi> </msub> </semantics> </math>. However, in <span class="html-italic">DVP Case</span>, after investigating the difference of <math display="inline"> <semantics> <msub> <mi mathvariant="script">V</mi> <mi>B</mi> </msub> </semantics> </math> between <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, surprisingly, we discovered <math display="inline"> <semantics> <mrow> <msubsup> <mi mathvariant="script">V</mi> <mrow> <mi>B</mi> </mrow> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </msubsup> <mo>&lt;</mo> <msubsup> <mi mathvariant="script">V</mi> <mrow> <mi>B</mi> </mrow> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics> </math>. That is to say, the value of <math display="inline"> <semantics> <msub> <mi mathvariant="script">V</mi> <mi>B</mi> </msub> </semantics> </math> is changed by time, in addition, a bigger <math display="inline"> <semantics> <msub> <mi mathvariant="script">V</mi> <mi>B</mi> </msub> </semantics> </math> can be obtained by another time. There is a big difference between waiting some time slots and without waiting. In previous assumption, we must put the <math display="inline"> <semantics> <mi mathvariant="script">V</mi> </semantics> </math> on <math display="inline"> <semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>V</mi> <mo>,</mo> <mi>E</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> at <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> which means they could not get the bigger <math display="inline"> <semantics> <msubsup> <mi mathvariant="script">V</mi> <mrow> <mi>B</mi> </mrow> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> </msubsup> </semantics> </math>. (<b>a</b>) Simple Case 1; (<b>b</b>) Simple Case 2; (<b>c</b>) DVP Case.</p> Full article ">Figure 6
<p>DataSet.</p> Full article ">Figure 7
<p>Experiments Results <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>P</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. (<b>a</b>) P2P; (<b>b</b>) EPINION; (<b>c</b>) BRIGHTKITE; (<b>d</b>) AMAZON.</p> Full article ">Figure 8
<p>Experiments Results <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>P</mi> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. (<b>a</b>) P2P; (<b>b</b>) EPINION; (<b>c</b>) BRIGHTKITE; (<b>d</b>) AMAZON.</p> Full article ">Figure 9
<p>Experiments Results <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>P</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>100</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. (<b>a</b>) P2P; (<b>b</b>) EPINION; (<b>c</b>) BRIGHTKITE; (<b>d</b>) AMAZON.</p> Full article ">Figure 10
<p>Experiments Results <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>P</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>200</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. (<b>a</b>) P2P; (<b>b</b>) EPINION; (<b>c</b>) BRIGHTKITE; (<b>d</b>) AMAZON.</p> Full article ">Figure 11
<p>Experiments Results <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <mi>P</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>500</mn> <mo stretchy="false">)</mo> </mrow> </semantics> </math>. (<b>a</b>) P2P; (<b>b</b>) EPINION; (<b>c</b>) BRIGHTKITE; (<b>d</b>) AMAZON.</p> Full article ">
1714 KiB  
Article
A Robust and Device-Free System for the Recognition and Classification of Elderly Activities
by Fangmin Li, Mohammed Abdulaziz Aide Al-qaness, Yong Zhang, Bihai Zhao and Xidao Luan
Sensors 2016, 16(12), 2043; https://doi.org/10.3390/s16122043 - 1 Dec 2016
Cited by 30 | Viewed by 6146
Abstract
Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, [...] Read more.
Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>System architecture and work flow.</p> Full article ">Figure 2
<p>Extracted Channel State Information (CSI) streams from fall experiment. (<b>a</b>) Original CSI streams; (<b>b</b>) CSI streams after exponential filter.</p> Full article ">Figure 3
<p>CSI of Walk experiments and Principle Components Analysis (PCA). (<b>a</b>) Original CSI amplitude; (<b>b</b>) Original CSI phase; (<b>c</b>) Principle Components (PCs) of CSI amplitude; (<b>d</b>) Principle Components (PCs) of CSI phase.</p> Full article ">Figure 4
<p>Experiment environment and settings. (<b>a</b>) Line-of-sight (LOS) scenario; (<b>b</b>) Non-line-of-sight (NLOS) scenario.</p> Full article ">Figure 5
<p>The confusion matrices in both LOS and NLOS scenarios. (<b>a</b>) The confusion matrix in an LOS scenario; (<b>b</b>) the confusion matrix in an NLOS scenario.</p> Full article ">Figure 6
<p>Precision, recall, and F-measure results. (<b>a</b>) The results of precision, recall, and F-measure in an LOS scenario; and (<b>b</b>) the results of precision, recall, and F-measure in an NLOS scenario.</p> Full article ">Figure 7
<p>Comparison of system precision with PCA method and without PCA. (<b>a</b>) Comparison results in the LOS scenario; (<b>b</b>) Comparison results in the NLOS scenario.</p> Full article ">Figure 8
<p>Channel State Information (CSI) vs. Received Signal Strength Indicator (RSSI) in an LOS scenario.</p> Full article ">
386 KiB  
Article
Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
by Bin Lyu, Zhen Yang, Guan Gui and Youhong Feng
Sensors 2016, 16(12), 2016; https://doi.org/10.3390/s16122016 - 29 Nov 2016
Cited by 15 | Viewed by 4673
Abstract
This paper considers a backscatter communication (BackCom) system including a reader and N tags, where each tag receives excitation signals transmitted by the reader and concurrently backscatters information to the reader in time-division-multiple-access (TDMA) mode. In this system, we aim to maximize the [...] Read more.
This paper considers a backscatter communication (BackCom) system including a reader and N tags, where each tag receives excitation signals transmitted by the reader and concurrently backscatters information to the reader in time-division-multiple-access (TDMA) mode. In this system, we aim to maximize the total system goodput by jointly optimizing reader transmission power, time allocation, and reflection ratio for the cases of passive and semi-passive tags. For each case, an optimization problem is formulated, which is non-convex and can be solved by being decomposed into at most N feasible sub-problems based on the priority of allocated reader transmission power. First, for the passive tags case, by solving the convex sub-problems sequentially and comparing their maximum total goodput, we derive the optimal resource allocation policy. Then, for the semi-passive tags case, we find a close-to-optimal solution, since each sub-problem can be reformulated as a biconvex problem, which is solved by a proposed block coordinate descent (BCD)-based optimization algorithm. Finally, simulation results demonstrate the superiority of the proposed resource allocation policies. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Multi-user backscatter communication system.</p> Full article ">Figure 2
<p>The working model of an activated tag.</p> Full article ">Figure 3
<p>Average total goodput vs. average reader transmission power with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>Average total goodput vs. average reader transmission power with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>.</p> Full article ">
1323 KiB  
Article
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks
by Heyuan Shi, Xiaoyu Song, Ming Gu and Jiaguang Sun
Sensors 2016, 16(12), 2013; https://doi.org/10.3390/s16122013 - 28 Nov 2016
Cited by 2 | Viewed by 4619
Abstract
The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats [...] Read more.
The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>The participatory sensing system with multiple trading platforms, where the task publishers can choose different platforms for their sensing tasks.</p> Full article ">Figure 2
<p>The process of a trading platform in VPSN.</p> Full article ">Figure 3
<p>The motivation in 4 areas with 3 tasks and 3 participants.</p> Full article ">Figure 4
<p>The components in GTSA.</p> Full article ">Figure 5
<p>The crossover operation in GTSA.</p> Full article ">Figure 6
<p>The mutation operation in GTSA.</p> Full article ">Figure 7
<p>Time cost of GTSA.</p> Full article ">Figure 8
<p>The profit gained when maximal payment of a task is 100.</p> Full article ">Figure 9
<p>The profit gained when maximal payment of a task is 50.</p> Full article ">Figure 10
<p>The profit gained when maximal payment of a task is 10.</p> Full article ">Figure 11
<p>The number of accepted tasks under different maximal payment of tasks.</p> Full article ">Figure 12
<p>The number of recruited participants under different maximal payment of tasks.</p> Full article ">Figure 13
<p>The impact of data reliability to GTSA based on 100 tasks.</p> Full article ">
571 KiB  
Article
On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
by Xuran Li, Hong-Ning Dai, Hao Wang and Hong Xiao
Sensors 2016, 16(12), 1987; https://doi.org/10.3390/s16121987 - 24 Nov 2016
Cited by 7 | Viewed by 4186
Abstract
Wireless sensor networks (WSNs) play an important role in Cyber Physical Social Sensing (CPSS) systems. An eavesdropping attack is one of the most serious threats to WSNs since it is a prerequisite for other malicious attacks. In this paper, we propose a novel [...] Read more.
Wireless sensor networks (WSNs) play an important role in Cyber Physical Social Sensing (CPSS) systems. An eavesdropping attack is one of the most serious threats to WSNs since it is a prerequisite for other malicious attacks. In this paper, we propose a novel anti-eavesdropping mechanism by introducing friendly jammers to wireless sensor networks (WSNs). In particular, we establish a theoretical framework to evaluate the eavesdropping risk of WSNs with friendly jammers and that of WSNs without jammers. Our theoretical model takes into account various channel conditions such as the path loss and Rayleigh fading, the placement schemes of jammers and the power controlling schemes of jammers. Extensive results show that using jammers in WSNs can effectively reduce the eavesdropping risk. Besides, our results also show that the appropriate placement of jammers and the proper assignment of emitting power of jammers can not only mitigate the eavesdropping risk but also may have no significant impairment to the legitimate communications. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>FJ-Reg Scheme: every jammer is placed at a gray square. Note that we only show a part of the whole network.</p> Full article ">Figure 2
<p>FJ-Ran Scheme: every jammer is randomly placed according to homogeneous Poisson Point Process (PPP). Note that we only show a part of the whole network.</p> Full article ">Figure 3
<p>Probability of eavesdropping attacks <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>E</mi> <mo>)</mo> </mrow> </semantics> </math> with FJ-Ran scheme (PPP) versus Non-Jam scheme when <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> </mrow> </semantics> </math> with SINR threshold <span class="html-italic">T</span> ranging from <math display="inline"> <semantics> <mrow> <mn>0</mn> <mi>dB</mi> </mrow> </semantics> </math> to <math display="inline"> <semantics> <mrow> <mn>20</mn> <mi>dB</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>Probability of eavesdropping attacks <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>E</mi> <mo>)</mo> </mrow> </semantics> </math> with FJ-Reg scheme (Grid) versus Non-Jam scheme when <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> </mrow> </semantics> </math> with SINR threshold <span class="html-italic">T</span> ranging from <math display="inline"> <semantics> <mrow> <mn>0</mn> <mi>dB</mi> </mrow> </semantics> </math> to <math display="inline"> <semantics> <mrow> <mn>20</mn> <mi>dB</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Probability of eavesdropping attacks <math display="inline"> <semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>E</mi> <mo>)</mo> </mrow> </semantics> </math> with FJ-PC scheme versus Non-Jam scheme when <math display="inline"> <semantics> <mrow> <mi>ζ</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>20</mn> </mrow> </semantics> </math> with SINR threshold <span class="html-italic">T</span> ranging from <math display="inline"> <semantics> <mrow> <mn>0</mn> <mi>dB</mi> </mrow> </semantics> </math> to <math display="inline"> <semantics> <mrow> <mn>20</mn> <mi>dB</mi> </mrow> </semantics> </math>.</p> Full article ">
14866 KiB  
Article
3D Tracking via Shoe Sensing
by Fangmin Li, Guo Liu, Jian Liu, Xiaochuang Chen and Xiaolin Ma
Sensors 2016, 16(11), 1809; https://doi.org/10.3390/s16111809 - 28 Oct 2016
Cited by 7 | Viewed by 5104
Abstract
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and [...] Read more.
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Amplitude of raw acceleration with sensor fixed on different position. (<b>a</b>) Fixed on thigh; (<b>b</b>) fixed on legs; (<b>c</b>) fixed on foot.</p> Full article ">Figure 1 Cont.
<p>Amplitude of raw acceleration with sensor fixed on different position. (<b>a</b>) Fixed on thigh; (<b>b</b>) fixed on legs; (<b>c</b>) fixed on foot.</p> Full article ">Figure 2
<p>Inertial sensor fixed on foot.</p> Full article ">Figure 3
<p>Energy waveforms with different window sizes: (<b>a</b>) window size of 21; (<b>b</b>) window size of 31; (<b>c</b>) window size of 41; (<b>d</b>) window size of 51.</p> Full article ">Figure 4
<p>Waveform of gait signal: (<b>a</b>) waveform of energy and gait; (<b>b</b>) acceleration and gait.</p> Full article ">Figure 5
<p>Reference coordinate system and sensor coordinate system.</p> Full article ">Figure 6
<p>Posture initialization process.</p> Full article ">Figure 7
<p>Convergent process. (<b>a</b>–<b>d</b>) describes the convergent process of the four elements in quaternion.</p> Full article ">Figure 8
<p>Posture estimate based on gait information.</p> Full article ">Figure 9
<p>Accumulated error elimination.</p> Full article ">Figure 10
<p>Based on cumulative error gait elimination.</p> Full article ">Figure 11
<p>Walking state classification model: (<b>a</b>) downstairs; (<b>b</b>) upstairs.</p> Full article ">Figure 12
<p><math display="inline"> <semantics> <msup> <mi>θ</mi> <mo>′</mo> </msup> </semantics> </math> angle contrast.</p> Full article ">Figure 13
<p>The vertical distance error while walking: (<b>a</b>) 100 sets of walking data; (<b>b</b>) vertical distance error.</p> Full article ">Figure 14
<p>The vertical distance error while running: (<b>a</b>) 100 sets of running data; (<b>b</b>) vertical distance error.</p> Full article ">Figure 15
<p>Vertical error accumulation.</p> Full article ">Figure 16
<p>Plane vertical distance error elimination process.</p> Full article ">Figure 17
<p>The vertical distance error elimination.</p> Full article ">Figure 18
<p>Collection node and network node: (<b>a</b>) collection node; (<b>b</b>) network node.</p> Full article ">Figure 19
<p>Experimental scene.</p> Full article ">Figure 20
<p>Comparative information extraction gaits.</p> Full article ">Figure 21
<p>Gait information extraction accuracy.</p> Full article ">Figure 22
<p>Traveling state determining statistical accuracy.</p> Full article ">Figure 23
<p>Walking normally: vertical distance error elimination: (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 24
<p>Jogging: vertical distance error elimination: (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 25
<p>Track of walking on a horizontal plane: (<b>a</b>) walking straight; (<b>b</b>) walking along square.</p> Full article ">Figure 26
<p>Normal walking step statistics: (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 27
<p>Jogging step statistics: (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 28
<p>Upstairs and downstairs tracks: (<b>a</b>) upstairs; (<b>b</b>) downstairs.</p> Full article ">Figure 29
<p>Horizontal step statistics (upstairs): (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 30
<p>Vertical step statistics (upstairs): (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 31
<p>Horizontal step statistics (downstairs): (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 32
<p>Vertical step statistics (downstairs): (<b>a</b>) distance error; (<b>b</b>) error percentage.</p> Full article ">Figure 33
<p>Walking heading angle error: (<b>a</b>) course error; (<b>b</b>) error percentage.</p> Full article ">Figure 34
<p>Jogging heading angle error: (<b>a</b>) course error; (<b>b</b>) error percentage.</p> Full article ">Figure 35
<p>3D positioning: (<b>a</b>) corridor structure; (<b>b</b>) 3D positioning trajectory in 3D modeling.</p> Full article ">
420 KiB  
Article
Combating QR-Code-Based Compromised Accounts in Mobile Social Networks
by Dong Guo, Jian Cao, Xiaoqi Wang, Qiang Fu and Qiang Li
Sensors 2016, 16(9), 1522; https://doi.org/10.3390/s16091522 - 20 Sep 2016
Cited by 5 | Viewed by 6592
Abstract
Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two [...] Read more.
Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two types of methods to identify compromised accounts in MSNs: one type is to analyze the potential threats on wireless access points and the potential threats on handheld devices’ operation systems so as to stop compromised accounts from spreading malicious messages; the other type is to apply the method of detecting compromised accounts in online social networks to MSNs. The above types of methods above focus neither on the problems of MSNs themselves nor on the interaction of sensors’ messages, which leads to the restrictiveness of platforms and the simplification of methods. In order to stop the spreading of compromised accounts in MSNs effectively, the attacks have to be traced to their sources first. Through sensors, users exchange information in MSNs and acquire information by scanning QR codes. Therefore, analyzing the traces of sensor-related information helps to identify the compromised accounts in MSNs. This paper analyzes the diversity of information sending modes of compromised accounts and normal accounts, analyzes the regularity of GPS (Global Positioning System)-based location information, and introduces the concepts of entropy and conditional entropy so as to construct an entropy-based model based on machine learning strategies. To achieve the goal, about 500,000 accounts of Sina Weibo and about 100 million corresponding messages are collected. Through the validation, the accuracy rate of the model is proved to be as high as 87.6%, and the false positive rate is only 3.7%. Meanwhile, the comparative experiments of the feature sets prove that sensor-based location information can be applied to detect the compromised accounts in MSNs. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>System overview.</p> Full article ">Figure 2
<p>Activities of message data.</p> Full article ">Figure 3
<p>Sample of items in sending messages.</p> Full article ">Figure 4
<p>Entropy of users’ behavior.</p> Full article ">Figure 5
<p>Regularity of users’ behavior.</p> Full article ">Figure 6
<p>Conditional entropy of location-based features.</p> Full article ">Figure 7
<p>Entropy of location-based features.</p> Full article ">
309 KiB  
Article
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications
by Tadesse Ghirmai
Sensors 2016, 16(9), 1454; https://doi.org/10.3390/s16091454 - 9 Sep 2016
Cited by 17 | Viewed by 5265
Abstract
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking [...] Read more.
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Sensor network layout.</p> Full article ">Figure 2
<p>Root Mean Square Error (RMSE) versus time.</p> Full article ">Figure 3
<p>Averaged Root Mean Square Error (ARMSE) versus particle size.</p> Full article ">Figure 4
<p>RMSE versus time for different iteration values of the consensus filter.</p> Full article ">Figure 5
<p>ARMSE versus number of iterations of the consensus filter.</p> Full article ">
1966 KiB  
Article
A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink
by Chuan Zhu, Sai Zhang, Guangjie Han, Jinfang Jiang and Joel J. P. C. Rodrigues
Sensors 2016, 16(9), 1432; https://doi.org/10.3390/s16091432 - 6 Sep 2016
Cited by 16 | Viewed by 5533
Abstract
Mobile sink is widely used for data collection in wireless sensor networks. It can avoid ‘hot spot’ problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is [...] Read more.
Mobile sink is widely used for data collection in wireless sensor networks. It can avoid ‘hot spot’ problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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<p>Categories of data collection algorithms with a mobile sink.</p> Full article ">Figure 2
<p>A wireless sensor network with virtual grid structure.</p> Full article ">Figure 3
<p>The relation of communication radius with side length of grid cell.</p> Full article ">Figure 4
<p>Format of <math display="inline"> <semantics> <mrow> <mi>H</mi> <mi>E</mi> <mi>L</mi> <mi>L</mi> <mi>O</mi> </mrow> </semantics> </math> packet.</p> Full article ">Figure 5
<p>The row column number (<math display="inline"> <semantics> <mrow> <mi>R</mi> <mi>C</mi> <mi>N</mi> </mrow> </semantics> </math>) of each virtual grid cell.</p> Full article ">Figure 6
<p>The direction number (<math display="inline"> <semantics> <mrow> <mi>D</mi> <mi>N</mi> </mrow> </semantics> </math>) of each grid cell.</p> Full article ">Figure 7
<p>The data packet structure of sensory data.</p> Full article ">Figure 8
<p>Flow chart of routing process.</p> Full article ">Figure 9
<p>The trajectory of the sink in one collecting period.</p> Full article ">Figure 10
<p>Sink moves to next column.</p> Full article ">Figure 11
<p>Flow chart of sink moving process.</p> Full article ">Figure 12
<p>The way of updating when the mobile sink moves along the column in one collecting period. (<b>a</b>) The mobile sink moves to the upside grid cell; (<b>b</b>) The head nodes of the marked grid cells are needed to be updated.</p> Full article ">Figure 13
<p>The way of updating when the mobile sink moves into another column when goes on to the next collecting period. (<b>a</b>) The mobile sink moves to the left neighbor column to start a new collecting period; (<b>b</b>) The head nodes of the marked grid cells are needed to be updated.</p> Full article ">Figure 14
<p>The impact of <math display="inline"> <semantics> <mrow> <mi>T</mi> <mi>h</mi> </mrow> </semantics> </math> on the network performance.</p> Full article ">Figure 15
<p>The impact of <math display="inline"> <semantics> <msub> <mi>r</mi> <mi>e</mi> </msub> </semantics> </math> on the network performance.</p> Full article ">Figure 16
<p>The impact of period of changing event area on the network performance.</p> Full article ">Figure 17
<p>The impact of velocity of source data on the network performance.</p> Full article ">Figure 18
<p>The impact of velocity of sink on the network performance.</p> Full article ">Figure 19
<p>Two application scenarios (<b>a</b>) Source nodes are distributed in a local area; (<b>b</b>) Source nodes are evenly distributed.</p> Full article ">Figure 20
<p>The comparison with VGDRA when source nodes are distributed unevenly. (<b>a</b>) Lifetime of network vs. the number of nodes; (<b>b</b>) Average residual energy vs. the number of nodes; (<b>c</b>) Variance of residual energy vs. the number of nodes.</p> Full article ">Figure 21
<p>The comparison with VGDRA when source nodes are evenly distributed. (<b>a</b>) Lifetime of network vs. the number of nodes; (<b>b</b>) Average residual energy vs. the number of nodes; (<b>c</b>) Variance of residual energy vs. the number of nodes.</p> Full article ">Figure 22
<p>The comparison with VGDRA when only the updating energy is considered.</p> Full article ">
1333 KiB  
Article
Queuing Theory Based Co-Channel Interference Analysis Approach for High-Density Wireless Local Area Networks
by Jie Zhang, Guangjie Han and Yujie Qian
Sensors 2016, 16(9), 1348; https://doi.org/10.3390/s16091348 - 23 Aug 2016
Cited by 17 | Viewed by 7829
Abstract
Increased co-channel interference (CCI) in wireless local area networks (WLANs) is bringing serious resource constraints to today’s high-density wireless environments. CCI in IEEE 802.11-based networks is inevitable due to the nature of the carrier sensing mechanism however can be reduced by resource optimization [...] Read more.
Increased co-channel interference (CCI) in wireless local area networks (WLANs) is bringing serious resource constraints to today’s high-density wireless environments. CCI in IEEE 802.11-based networks is inevitable due to the nature of the carrier sensing mechanism however can be reduced by resource optimization approaches. That means the CCI analysis is basic, but also crucial for an efficient resource management. In this article, we present a novel CCI analysis approach based on the queuing theory, which considers the randomness of end users’ behavior and the irregularity and complexity of network traffic in high-density WLANs that adopts the M/M/c queuing model for CCI analysis. Most of the CCIs occur when multiple networks overlap and trigger channel contentions; therefore, we use the ratio of signal-overlapped areas to signal coverage as a probabilistic factor to the queuing model to analyze the CCI impacts in highly overlapped WLANs. With the queuing model, we perform simulations to see how the CCI influences the quality of service (QoS) in high-density WLANs. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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Figure 1

Figure 1
<p>Example of co-channel interference in WLANs: (<b>a</b>) data communication topology; (<b>b</b>) carrier sensing topology; (<b>c</b>) cases of CCI through neighboring AP and neighboring client station.</p> Full article ">Figure 2
<p>A general queuing model of Kendall’s notation.</p> Full article ">Figure 3
<p>Relationship between queuing model and CCI analysis.</p> Full article ">Figure 4
<p>OCR calculation method, overlapped area among two BSSs’ signal coverage can be calculated by measuring the distance between the APs.</p> Full article ">Figure 5
<p>An example of network topology for queuing simulations.</p> Full article ">Figure 6
<p>Averaged packet transmission delay in fixed mean-density WLANs with different OCR levels.</p> Full article ">Figure 7
<p>Comparison of averaged transmission delay of arrived packets.</p> Full article ">Figure 8
<p>Comparison of maximum queuing delay of arrived packets.</p> Full article ">Figure 9
<p>Comparison of racket retransmission rate.</p> Full article ">Figure 10
<p>Comparison of number of waiting packets in queue.</p> Full article ">Figure 11
<p>Rate of number of processed packets to number of arrived packets.</p> Full article ">Figure 12
<p>Comparison of bandwidth utilization rate.</p> Full article ">Figure 13
<p>Comparison of averaged delay and number of processed packets in different OCR cases.</p> Full article ">
4636 KiB  
Article
Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks
by Jin Yang, Fagui Liu, Jianneng Cao and Liangming Wang
Sensors 2016, 16(7), 1081; https://doi.org/10.3390/s16071081 - 14 Jul 2016
Cited by 13 | Viewed by 6604
Abstract
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance [...] Read more.
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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Figure 1

Figure 1
<p>Illustration of (<b>a</b>) sink queries MWSN; (<b>b</b>) graphic description of MWSN.</p> Full article ">Figure 2
<p>(<b>a</b>) Illustration for routing tree; (<b>b</b>) Routing recover for mobile moves away; (<b>c</b>) Routing recover for relay node failed.</p> Full article ">Figure 3
<p>Flowchart of new routing protocol.</p> Full article ">Figure 4
<p>(<b>a</b>) Network topology; (<b>b</b>) Position vector encoded for (<b>a</b>); (<b>c</b>) Routing tree decoded from (<b>b</b>).</p> Full article ">Figure 5
<p>Multi routing paths share some relay nodes.</p> Full article ">Figure 6
<p>Compare of convergence.</p> Full article ">Figure 7
<p>Average packet delivery ratio with respect to different node failure probabilities. (<b>a</b>) When the node failure probability is 0.01; (<b>b</b>) When the node failure probability is 0.02; (<b>c</b>) When the node failure probability is 0.04; (<b>d</b>) Average PDR comparative advantage.</p> Full article ">Figure 8
<p>Average end-to-end delay with respect to different node failure probabilities. (<b>a</b>) When the node failure probability is 0.01; (<b>b</b>) When the node failure probability is 0.02; (<b>c</b>) When the node failure probability is 0.04; (<b>d</b>) Average EED comparative advantage.</p> Full article ">Figure 9
<p>Average energy consumption ratio (ECR) with respect to different speeds of mobile sinks. (<b>a</b>) When the moving speed of sinks is 5 m/s; (<b>b</b>) When the moving speed of sinks is 10 m/s; (<b>c</b>) When the moving speed of sinks is 20 m/s; (<b>d</b>) Average ECR comparative advantage.</p> Full article ">
14953 KiB  
Article
A Cyber-Physical System for Girder Hoisting Monitoring Based on Smartphones
by Ruicong Han, Xuefeng Zhao, Yan Yu, Quanhua Guan, Weitong Hu and Mingchu Li
Sensors 2016, 16(7), 1048; https://doi.org/10.3390/s16071048 - 7 Jul 2016
Cited by 30 | Viewed by 5751
Abstract
Offshore design and construction is much more difficult than land-based design and construction, particularly due to hoisting operations. Real-time monitoring of the orientation and movement of a hoisted structure is thus required for operators’ safety. In recent years, rapid development of the smart-phone [...] Read more.
Offshore design and construction is much more difficult than land-based design and construction, particularly due to hoisting operations. Real-time monitoring of the orientation and movement of a hoisted structure is thus required for operators’ safety. In recent years, rapid development of the smart-phone commercial market has offered the possibility that everyone can carry a mini personal computer that is integrated with sensors, an operating system and communication system that can act as an effective aid for cyber-physical systems (CPS) research. In this paper, a CPS for hoisting monitoring using smartphones was proposed, including a phone collector, a controller and a server. This system uses smartphones equipped with internal sensors to obtain girder movement information, which will be uploaded to a server, then returned to controller users. An alarming system will be provided on the controller phone once the returned data exceeds a threshold. The proposed monitoring system is used to monitor the movement and orientation of a girder during hoisting on a cross-sea bridge in real time. The results show the convenience and feasibility of the proposed system. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Elevation of the cross-sea bridge investigated in this study.</p> Full article ">Figure 2
<p>Flowchart of the monitoring and alarm systems.</p> Full article ">Figure 3
<p>Acceleration directions.</p> Full article ">Figure 4
<p>Angle-of-rotation directions.</p> Full article ">Figure 5
<p>Photo of calibration experiment.</p> Full article ">Figure 6
<p>Angle variation with step-by-step method. (<b>a</b>) Angle around <span class="html-italic">x</span>-axis; (<b>b</b>) Angle around <span class="html-italic">y</span>-axis.</p> Full article ">Figure 7
<p>Angle step comparison between smart phone and angle instrument. (<b>a</b>) Angle step around <span class="html-italic">x</span>-axis; (<b>b</b>) Angle step around <span class="html-italic">y</span>-axis.</p> Full article ">Figure 8
<p>Controller interface.</p> Full article ">Figure 9
<p>Interface of the real-time monitoring system.</p> Full article ">Figure 10
<p>Collector interface of the iPhone. (<b>a</b>) GPS information; (<b>b</b>) Gyroscope interface.</p> Full article ">Figure 11
<p>Schematic of the hoisting realization process.</p> Full article ">Figure 12
<p>Specified directions.</p> Full article ">Figure 13
<p>Location of the second girder.</p> Full article ">Figure 14
<p>On-site hoisting.</p> Full article ">Figure 15
<p>Arrangement of the collector (i.e., the iPhone 5S).</p> Full article ">Figure 16
<p>Algorithm of side-span hoisting.</p> Full article ">Figure 17
<p>Vertical-acceleration time-history curve of the girder during hoisting.</p> Full article ">Figure 18
<p>Angle about the <span class="html-italic">x</span>-axis of the girder during hoisting.</p> Full article ">Figure 19
<p>Angle about the <span class="html-italic">y-</span>axis of the girder during hoisting.</p> Full article ">Figure 20
<p>Bolt.</p> Full article ">Figure 21
<p>On-site hoisting of the middle span.</p> Full article ">Figure 22
<p>Arrangement of the collector.</p> Full article ">Figure 23
<p>Position of collector and controller.</p> Full article ">Figure 24
<p>Algorithm of middle-span hoisting monitoring.</p> Full article ">Figure 25
<p>Vertical-acceleration time-history curve.</p> Full article ">Figure 26
<p>Angle about the <span class="html-italic">x</span>-axis of the girder.</p> Full article ">Figure 27
<p>Angle about the <span class="html-italic">y</span>-axis of the girder.</p> Full article ">Figure 28
<p>Height difference in the left/right direction.</p> Full article ">Figure 29
<p>Height difference in the front/back direction.</p> Full article ">
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