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Prof. Dr. Antonio Cano-Ortega

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Electrical Engineering Department, University of Jaen, Campus Las Lagunillas, s/n, 23071 Jaen, Spain

Prof. Dr. Francisco Sánchez-Sutil

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Electrical Engineering Department, University of Jaen, Campus Las Lagunillas, s/n, 23071 Jaen, Spain

IoT for Energy Management Systems and Smart Cities

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closed (30 June 2023)

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closed (30 August 2023)

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Topic IoT for Energy Management Systems and Smart Cities book cover image

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Topic Information

Dear Colleagues,

Smart cities represent a great advance in terms of sustainability, energy efficiency, and being able to respond to the needs of enterprises, institutions, and inhabitants.

In this sense, smart grids contribute to the development of smart cities in the field of electrical energy, including concepts such as renewable energies, distributed generation, energy efficiency, and smart homes and automation.

In order to be able to implement all the functionalities of smart grids, it is necessary to have real-time information on the different installations. In this sense, IoT plays a fundamental role in developing smart grids.

Cloud computing, which integrates the data obtained with smart electrical meters, smart electrical power analyzers, and other intelligent metering devices, contributes to the availability of the measured data in real time and provides intelligence to existing electrical networks.

Wireless communication networks, especially LPWAN, allow the construction of devices with low energy consumption and high operating autonomy, which can be installed in different locations even with difficult access.

The massive implantation of the electric vehicle implies the construction of charging stations. These stations must use renewable energy sources that contribute to saving fossil fuels, reducing CO2, and increasing the sustainability of electric mobility.

Hybrid storage systems, together with renewable energies, constitute new development systems, in which it is necessary to measure electrical variables and control the operation of the system.

Prof. Dr. Antonio Cano-Ortega
Prof. Dr. Francisco Sánchez-Sutil
Topic Editors

Keywords

cloud computing
smart electric meters
smart power analyzers
smart grids for smart cities
smart home and automation
monitoring and control renewable energy
public lighting system
distributed generation
hybrid electric energy storage systems
electric vehicle charging stations
wireless technologies

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000
IoT IoT	-	8.5	2020	15.9 Days	CHF 1200

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Published Papers (22 papers)

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46 pages, 4628 KiB

Open AccessReview

Global Models of Smart Cities and Potential IoT Applications: A Review

by Ahmed Hassebo and Mohamed Tealab

IoT 2023, 4(3), 366-411; https://doi.org/10.3390/iot4030017 - 31 Aug 2023

Cited by 11 | Viewed by 10144

As the world becomes increasingly urbanized, the development of smart cities and the deployment of IoT applications will play an essential role in addressing urban challenges and shaping sustainable and resilient urban environments. However, there are also challenges to overcome, including privacy and [...] Read more.

As the world becomes increasingly urbanized, the development of smart cities and the deployment of IoT applications will play an essential role in addressing urban challenges and shaping sustainable and resilient urban environments. However, there are also challenges to overcome, including privacy and security concerns, and interoperability issues. Addressing these challenges requires collaboration between governments, industry stakeholders, and citizens to ensure the responsible and equitable implementation of IoT technologies in smart cities. The IoT offers a vast array of possibilities for smart city applications, enabling the integration of various devices, sensors, and networks to collect and analyze data in real time. These applications span across different sectors, including transportation, energy management, waste management, public safety, healthcare, and more. By leveraging IoT technologies, cities can optimize their infrastructure, enhance resource allocation, and improve the quality of life for their citizens. In this paper, eight smart city global models have been proposed to guide the development and implementation of IoT applications in smart cities. These models provide frameworks and standards for city planners and stakeholders to design and deploy IoT solutions effectively. We provide a detailed evaluation of these models based on nine smart city evaluation metrics. The challenges to implement smart cities have been mentioned, and recommendations have been stated to overcome these challenges. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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54 pages, 12312 KiB

Open AccessReview

A Tutorial on Agricultural IoT: Fundamental Concepts, Architectures, Routing, and Optimization

by Emmanuel Effah, Ousmane Thiare and Alexander M. Wyglinski

IoT 2023, 4(3), 265-318; https://doi.org/10.3390/iot4030014 - 27 Jul 2023

Cited by 4 | Viewed by 3172

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via a systematic survey and synthesis of the related literature. The negative impacts of climate change and the [...] Read more.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via a systematic survey and synthesis of the related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on the specific performance and user expectations as well as technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault-tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a three-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>Seasonal failure probability-2014 [<a href="#B4-IoT-04-00014" class="html-bibr">4</a>] depicting the extent of climate change impact on Africa’s farmlands.</p> Full article ">Figure 2
<p>Generalized design expectations of WSN-based Agri-IoT technology.</p> Full article ">Figure 3
<p>Generalized Agri-IoT framework consisting of: field layout overview of Agri-IoT framework (<b>a</b>), sample of classic Agri-IoT in the state of the art (<b>b</b>), and key components of an SN or a BS (<b>c</b>).</p> Full article ">Figure 4
<p>Conceptual framework: Agri-IoT-based farm monitoring and control cycle.</p> Full article ">Figure 5
<p>Proposed Agri-IoT architectural layers with core components of Agri-IoT ecosystem and the “things” taxonomy.</p> Full article ">Figure 6
<p>Generalized taxonomy of IoT applications.</p> Full article ">Figure 7
<p>The roles of Agri-IoT in smart farming with specific use cases.</p> Full article ">Figure 8
<p>Different architectural layers in the state of the art of IoT ecosystem.</p> Full article ">Figure 9
<p>Principal design factors for Agri-IoT networks.</p> Full article ">Figure 10
<p>Proposed design objectives and strategies of WSN-based Agri-IoT routing protocols.</p> Full article ">Figure 11
<p>Taxonomy of WSN-based routing protocols of Agri-IoT.</p> Full article ">Figure 12
<p>Sample network architectures: centralized-data-centric, cluster-based, and graph/flooding-based architectural frameworks of WSN sublayer.</p> Full article ">Figure 13
<p>Proposed functionality-based MAC classification framework.</p> Full article ">Figure 14
<p>Fault–error–failure cycle [<a href="#B72-IoT-04-00014" class="html-bibr">72</a>].</p> Full article ">Figure 15
<p>Faults in the WSN sublayer of Agri-IoT: sources and fault propagation model.</p> Full article ">Figure 16
<p>Classification of faults in the state of the art and proposed fault taxonomies for WSN-based Agri-IoT.</p> Full article ">Figure 17
<p>FM framework in WSN sublayer of Agri-IoT.</p> Full article ">Figure 18
<p>Conceptual architectural framework of the proposed CA-IoT for precision irrigation management.</p> Full article ">Figure 19
<p>CA-IoT use case cluster illustrating the key network components: MNs, CH, and BS.</p> Full article ">Figure 20
<p>Characterization of cluster-based networks and deduced taxonomy of MOO metrics for optimizing Agri-IoT networks.</p> Full article ">Figure 21
<p>Proposed operation cycle for designing our CA-IoT network’s routing protocol.</p> Full article ">

23 pages, 46173 KiB

Open AccessArticle

IoT-Enabled Smart Drip Irrigation System Using ESP32

by Gilroy P. Pereira, Mohamed Z. Chaari and Fawwad Daroge

IoT 2023, 4(3), 221-243; https://doi.org/10.3390/iot4030012 - 7 Jul 2023

Cited by 13 | Viewed by 17373

Agriculture, or farming, is the science of cultivating the soil, growing crops, and raising livestock. Ever since the days of the first plow from sticks over ten thousand years ago, agriculture has always depended on technology. As technology and science improved, so did [...] Read more.

Agriculture, or farming, is the science of cultivating the soil, growing crops, and raising livestock. Ever since the days of the first plow from sticks over ten thousand years ago, agriculture has always depended on technology. As technology and science improved, so did the scale at which farming was possible. With the popularity and growth of the Internet of Things (IoT) in recent years, there are even more avenues for technology to make agriculture more efficient and help farmers in every nation. In this paper, we designed a smart IoT-enabled drip irrigation system using ESP32 to automate the irrigation process, and we tested it. The ESP32 communicates with the Blynk app, which is used to collect irrigation data, manually water the plants, switch off the automatic watering function, and plot graphs based on the readings of the sensors. We connected the ESP32 to a soil moisture sensor, temperature sensor, air humidity sensor, and water flow sensor. The ESP32 regularly checks if the soil is dry. If the soil is dry and the soil temperature is appropriate for watering, the ESP32 opens a solenoid valve and waters the plants. The amount of time to run the drip irrigation system is determined based on the flow rate measured by the water flow sensor. The ESP32 reads the humidity sensor values and notifies the user when the humidity is too high or too low. The user can switch off the automatic watering system according to the humidity value. In both primary and field tests, we found that the system ran well and was able to grow green onions. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>Overview of the IoT-enabled smart drip irrigation system [<a href="#B21-IoT-04-00012" class="html-bibr">21</a>].</p> Full article ">Figure 2
<p>The hourly reported temperature in Qatar for May 2023, color-coded into bands. The shaded overlays indicate night and civil twilight (source: <a href="http://www.weatherspark.com" target="_blank">www.weatherspark.com</a> (accessed on 13 May 2023)) [<a href="#B27-IoT-04-00012" class="html-bibr">27</a>].</p> Full article ">Figure 3
<p>Flow chart of the IoT-enabled smart drip irrigation system.</p> Full article ">Figure 4
<p>DFRobot moisture sensor.</p> Full article ">Figure 5
<p>DS18B20 waterproof temperature sensor.</p> Full article ">Figure 6
<p>DHT22 air humidity sensor.</p> Full article ">Figure 7
<p>FS300A G3/4 inch water flow sensor.</p> Full article ">Figure 8
<p>Hunter PGV-100G solenoid valve.</p> Full article ">Figure 9
<p>Preparing the acrylic container. (<b>a</b>) Curing the super glue and silicone. (<b>b</b>) Drainage holes drilled in the base of the container.</p> Full article ">Figure 10
<p>Adding soil to the container.</p> Full article ">Figure 11
<p>The addition of drip irrigation pipelines.</p> Full article ">Figure 12
<p>Measuring the temperature of a hot cup of water using the DS18B20 sensor and a thermal camera. (<b>a</b>) Temperature measured by the DS18B20 sensor. (<b>b</b>) Temperature measured by the thermal camera (FLIR C3-X, manufactured by Teledyne FLIR LLC, Wilsonville, OR, USA).</p> Full article ">Figure 13
<p>Water tank and pump motor for the irrigation system; (<b>a</b>) 400 US gallon water container; (<b>b</b>) water pump motor covered for water resistance.</p> Full article ">Figure 13 Cont.
<p>Water tank and pump motor for the irrigation system; (<b>a</b>) 400 US gallon water container; (<b>b</b>) water pump motor covered for water resistance.</p> Full article ">Figure 14
<p>Plastic container for the smart drip irrigation system. (<b>a</b>) Components of the irrigation system placed in the container. (<b>b</b>) Sealed container ready for outdoor use.</p> Full article ">Figure 15
<p>Making the irrigation system water- and dust-resistant. (<b>a</b>) Protecting the electronics with an acrylic container. (<b>b</b>) Protecting the solenoid valve and water flow sensor with an acrylic container. (<b>c</b>) Protecting the solenoid wires with a 3D-printed case. (<b>d</b>) Protecting the DHT22 with a plastic case.</p> Full article ">Figure 15 Cont.
<p>Making the irrigation system water- and dust-resistant. (<b>a</b>) Protecting the electronics with an acrylic container. (<b>b</b>) Protecting the solenoid valve and water flow sensor with an acrylic container. (<b>c</b>) Protecting the solenoid wires with a 3D-printed case. (<b>d</b>) Protecting the DHT22 with a plastic case.</p> Full article ">Figure 16
<p>Smart drip irrigation system working in the field.</p> Full article ">Figure 17
<p>Final flow chart of the smart drip irrigation system firmware.</p> Full article ">Figure 18
<p>Smart irrigation system used for growing spring onions.</p> Full article ">Figure 19
<p>Web dashboard for the smart drip irrigation system. (<b>a</b>) Switches, irrigation data, and temperature graph. (<b>b</b>) Humidity and moisture content graph.</p> Full article ">Figure 19 Cont.
<p>Web dashboard for the smart drip irrigation system. (<b>a</b>) Switches, irrigation data, and temperature graph. (<b>b</b>) Humidity and moisture content graph.</p> Full article ">Figure 20
<p>Soil moisture variation for one week.</p> Full article ">Figure 21
<p>Humidity fluctuation throughout the week.</p> Full article ">Figure 22
<p>Temperature fluctuation throughout the week.</p> Full article ">

19 pages, 1487 KiB

Open AccessArticle

An IoT- and Cloud-Based E-Waste Management System for Resource Reclamation with a Data-Driven Decision-Making Process

by Mithila Farjana, Abu Bakar Fahad, Syed Eftasum Alam and Md. Motaharul Islam

IoT 2023, 4(3), 202-220; https://doi.org/10.3390/iot4030011 - 6 Jul 2023

Cited by 18 | Viewed by 12015

IoT-based smart e-waste management is an emerging field that combines technology and environmental sustainability. E-waste is a growing problem worldwide, as discarded electronics can have negative impacts on the environment and public health. In this paper, we have proposed a smart e-waste management [...] Read more.

IoT-based smart e-waste management is an emerging field that combines technology and environmental sustainability. E-waste is a growing problem worldwide, as discarded electronics can have negative impacts on the environment and public health. In this paper, we have proposed a smart e-waste management system. This system uses IoT devices and sensors to monitor and manage the collection, sorting, and disposal of e-waste. The IoT devices in this system are typically embedded with sensors that can detect and monitor the amount of e-waste in a given area. These sensors can provide real-time data on e-waste, which can then be used to optimize collection and disposal processes. E-waste is like an asset to us in most cases; as it is recyclable, using it in an efficient manner would be a perk. By employing machine learning to distinguish e-waste, we can contribute to separating metallic and plastic components, the utilization of pyrolysis to transform plastic waste into bio-fuel, coupled with the generation of bio-char as a by-product, and the repurposing of metallic portions for the development of solar batteries. We can optimize its use and also minimize its environmental impact; it presents a promising avenue for sustainable waste management and resource recovery. Our proposed system also uses cloud-based platforms to help analyze patterns and trends in the data. The Autoregressive Integrated Moving Average, a statistical method used in the cloud, can provide insights into future garbage levels, which can be useful for optimizing waste collection schedules and improving the overall process. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>System architecture of our proposed solution.</p> Full article ">Figure 2
<p>Data-driven decision making process.</p> Full article ">Figure 3
<p>System architecture of collecting and monitoring trash using cloud and IoT.</p> Full article ">Figure 4
<p>Flowchart of Proposed System.</p> Full article ">Figure 5
<p>Graphical analysis of e-waste level update.</p> Full article ">Figure 6
<p>E-waste level update information in cloud.</p> Full article ">Figure 7
<p>E-waste level update information in cloud with timestamp.</p> Full article ">Figure 8
<p>E-waste level update information in cloud showing in serial monitor.</p> Full article ">Figure 9
<p>Precision of each category.</p> Full article ">Figure 10
<p>Recall of each category.</p> Full article ">Figure 11
<p>F1-Score (%) of each category.</p> Full article ">Figure 12
<p>Overall performance of each category.</p> Full article ">Figure 13
<p>Yield of bio-fuel from plastic waste using pyrolysis method.</p> Full article ">Figure 14
<p>Reduction in CO<math display="inline"><semantics><msub><mrow/><mn>2</mn></msub></semantics></math> emissions with metal recycling from e-waste for solar batteries.</p> Full article ">

23 pages, 13289 KiB

Open AccessArticle

Unified Environment for Real Time Control of Hybrid Energy System Using Digital Twin and IoT Approach

by Lamine Chalal, Allal Saadane and Ahmed Rachid

Sensors 2023, 23(12), 5646; https://doi.org/10.3390/s23125646 - 16 Jun 2023

Cited by 4 | Viewed by 2582

Today, climate change combined with the energy crisis is accelerating the worldwide adoption of renewable energies through incentive policies. However, due to their intermittent and unpredictable behavior, renewable energy sources need EMS (energy management systems) as well as storage infrastructure. In addition, their [...] Read more.

Today, climate change combined with the energy crisis is accelerating the worldwide adoption of renewable energies through incentive policies. However, due to their intermittent and unpredictable behavior, renewable energy sources need EMS (energy management systems) as well as storage infrastructure. In addition, their complexity requires the implementation of software and hardware means for data acquisition and optimization. The technologies used in these systems are constantly evolving but their current maturity level already makes it possible to design innovative approaches and tools for the operation of renewable energy systems. This work focuses on the use of Internet of Things (IoT) and Digital Twin (DT) technologies for standalone photovoltaic systems. Based on Energetic Macroscopic Representation (EMR) formalism and the Digital Twin (DT) paradigm, we propose a framework to improve energy management in real time. In this article, the digital twin is defined as the combination of the physical system and its digital model, communicating data bi-directionally. Additionally, the digital replica and IoT devices are coupled via MATLAB Simulink as a unified software environment. Experimental tests are carried out to validate the efficiency of the digital twin developed for an autonomous photovoltaic system demonstrator. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>Diagram of the digital twin of the standalone PV system.</p> Full article ">Figure 2
<p>PV system demonstrator.</p> Full article ">Figure 3
<p>Control system architecture of the PV plant.</p> Full article ">Figure 4
<p>Digital Twin architecture overview.</p> Full article ">Figure 5
<p>Equivalent circuit model. (<b>a</b>) PV cell; (<b>b</b>) PV panel composed of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>n</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> cells in series.</p> Full article ">Figure 6
<p>Actual data vs. data computed by the model: (<b>a</b>) I-V characteristics from datasheet; (<b>b</b>) actual I-V characteristics vs. computed I-V characteristics; (<b>c</b>) actual P-V characteristics vs. computed P-V characteristics.</p> Full article ">Figure 7
<p>Experimental test and comparison of I-V curves: (<b>a</b>) experimental components of the tested PV panel; (<b>b</b>) comparison between real test and the mathematical model.</p> Full article ">Figure 8
<p>Nonlinear Battery model.</p> Full article ">Figure 9
<p>(<b>a</b>) Discharge curve (Q-V); (<b>b</b>) Discharge curve (Hours-V).</p> Full article ">Figure 10
<p>Simplified PV system architecture.</p> Full article ">Figure 11
<p>Topology of stand-alone hybrid PV system.</p> Full article ">Figure 12
<p>EMR of the studied hybrid system.</p> Full article ">Figure 13
<p>Inversion-based control principle.</p> Full article ">Figure 14
<p>EMR and deduced control of the studied hybrid system.</p> Full article ">Figure 15
<p>(<b>a</b>) PV Power versus solar panel voltage for different irradiance (T = 25°) (<b>b</b>) Flowchart of perturb and observe algorithm.</p> Full article ">Figure 16
<p>MATLAB Simulink model of the studied hybrid system and its control.</p> Full article ">Figure 17
<p>(<b>a</b>) Monitored irradiance and temperature profiles; (<b>b</b>) PV, battery, and load power curves.</p> Full article ">Figure 18
<p>(<b>a</b>) PV, Battery, and DC load currents; (<b>b</b>) Battery state of charge.</p> Full article ">Figure 19
<p>Applied solar irradiance (artificial light source).</p> Full article ">Figure 20
<p>Digital model PV current vs measured PV current.</p> Full article ">Figure 21
<p>Digital model vs measured load currents.</p> Full article ">Figure 22
<p>Digital model battery current vs measured battery current.</p> Full article ">Figure 23
<p>Digital model data vs measured PV, battery, and load power.</p> Full article ">Figure 24
<p>Rule-based algorithm flowchart.</p> Full article ">Figure 25
<p>Measured irradiance.</p> Full article ">Figure 26
<p>Measured PV, battery, and load power.</p> Full article ">Figure 27
<p>(<b>a</b>) Battery voltage; (<b>b</b>) Battery state of charge.</p> Full article ">

15 pages, 670 KiB

Open AccessArticle

Evaluation of IoT-Based Smart Home Assistance for Elderly People Using Robot

by Abdulrahman A. Alshdadi

Electronics 2023, 12(12), 2627; https://doi.org/10.3390/electronics12122627 - 11 Jun 2023

Cited by 1 | Viewed by 2470

In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The [...] Read more.

In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The features of a smart home system with IoT technology include temperature control, lighting control, surveillance, security, smart electricity, and water sensors. Nowadays, elderly people may not be able to maintain their homes appropriately and may feel uncomfortable performing day-to-day activities. Therefore, Cyborg can be used to carry out the activities of elderly people. Such activities include switching off unnecessary lights, watering plants, gas control, monitoring intruders or unknown persons, alerting elderly people in emergency situations, etc. These activities are controlled by web-based platforms as well as smartphone applications. The issues with the existing algorithms include that they are inefficient, require a long time for implementation, and have high storage space requirements. This paper proposes the k-nearest neighbors (KNN) with an artificial bee colony (ABC) algorithm (KNN-ABC). In this proposed work, KNN-ABC is used with wireless sensor devices for perceiving the surroundings of the smart home. This work implements the automatic control of electronic appliances, alert signal processors, intruder detection, and performs day-to-day activities automatically in an efficient way. GNB for intruder detection in the smart home environment system using the Cyborg human intervention robot achieved an accuracy rate of 88.12%, the Artificial Bee Colony algorithm (ABC) achieved 90.12% accuracy on the task of power saving in smart home electronic appliances, the KNN technique achieved 91.45% accuracy on the task of providing a safer pace to the elderly in the smart home environment system, and our proposed KNN-ABC system achieved 93.72%. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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19 pages, 3277 KiB

Open AccessArticle

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

by Victor Ariel Leal Sobral, Jacob Nelson, Loza Asmare, Abdullah Mahmood, Glen Mitchell, Kwadwo Tenkorang, Conor Todd, Bradford Campbell and Jonathan L. Goodall

Smart Cities 2023, 6(3), 1416-1434; https://doi.org/10.3390/smartcities6030068 - 19 May 2023

Cited by 6 | Viewed by 3494

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud [...] Read more.

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>IoT system architecture diagram for the Radon gas monitoring application.</p> Full article ">Figure 2
<p>IoT system architecture diagram for the stormwater monitoring application.</p> Full article ">Figure 3
<p>Our system architecture diagram using Amazon Web Services and The Things Network.</p> Full article ">Figure 4
<p>Entity relationship diagram for database design.</p> Full article ">Figure 5
<p>Grafana decision support dashboard of a water depth monitoring sensor.</p> Full article ">Figure 6
<p>Example of parameters for the sensor data download API, with the asterisk representing the required authorization token field.</p> Full article ">Figure 7
<p>Response from API using example parameters.</p> Full article ">Figure 8
<p>S3 storage costs with varying parameters. Plots (<b>a</b>,<b>c</b>) evaluate the total cost of S3 data storage at the end of 5 years. Plot (<b>b</b>,<b>d</b>) assume devices with sampling rate of 4800 samples per month.</p> Full article ">

23 pages, 2107 KiB

Open AccessArticle

Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

by Matteo Caldera, Asad Hussain, Sabrina Romano and Valerio Re

Energies 2023, 16(2), 824; https://doi.org/10.3390/en16020824 - 11 Jan 2023

Cited by 6 | Viewed by 5160

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer [...] Read more.

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>Smart home: representation of sensors.</p> Full article ">Figure 2
<p>Percentage of available data for Dataset A.</p> Full article ">Figure 3
<p>Percentage of available data for Dataset B.</p> Full article ">Figure 4
<p>Flow chart of the numerical model.</p> Full article ">Figure 5
<p>Cumulative distribution of the energy consumption.</p> Full article ">Figure 6
<p>Automatic identification of the phases in the consumption profile of a single washing cycle of a washing machine.</p> Full article ">Figure 7
<p>Automatic detection of two consecutive washing cycles.</p> Full article ">Figure 8
<p>Duration, start time, and energy consumption of a washing machine’s cycles.</p> Full article ">Figure 9
<p>Subdivision of records (upper graph) and consumption (lower graph) based on the type of operation for a washing machine.</p> Full article ">Figure 10
<p>Monthly subdivision of records based on the type of operation for a washing machine.</p> Full article ">Figure 11
<p>Monthly allocation of consumption in the three time slots defined by ARERA.</p> Full article ">Figure 12
<p>Monthly allocation of consumption according to the user-defined daytime slots.</p> Full article ">Figure 13
<p>Energy recorded and extrapolated by the model for different dishwashers.</p> Full article ">Figure 14
<p>Percentage of short and long cycles for dishwashers.</p> Full article ">Figure 15
<p>Energy consumption in short and long cycles for dishwashers.</p> Full article ">Figure 16
<p>Comparison of energy consumption cycles occurred in close succession for the washing machine.</p> Full article ">Figure 17
<p>Feedback on the DHOMUS platform regarding the use of washing machine (in Italian).</p> Full article ">Figure 18
<p>Section of the DHOMUS dashboard reporting the user’s consumption vs. average consumption of other similar users (in Italian).</p> Full article ">

12 pages, 1852 KiB

Open AccessArticle

Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System

by Shabana Habib, Saleh Alyahya, Muhammad Islam, Abdullah M. Alnajim, Abdulatif Alabdulatif and Abdullah Alabdulatif

Electronics 2023, 12(1), 28; https://doi.org/10.3390/electronics12010028 - 22 Dec 2022

Cited by 12 | Viewed by 4494

Automation is being fueled by a multifaceted approach to technological advancements, which includes advances in artificial intelligence, robotics, sensors, and cloud computing. The use of automated, as opposed to conventional, systems, has become more popular in recent years. Modern agricultural technology has played [...] Read more.

Automation is being fueled by a multifaceted approach to technological advancements, which includes advances in artificial intelligence, robotics, sensors, and cloud computing. The use of automated, as opposed to conventional, systems, has become more popular in recent years. Modern agricultural technology has played an important role in the development of Saudi Arabia in addition to upgrading infrastructure and plans. Agriculture in Saudi Arabia is dependent upon wells, which are insufficient in terms of water supplies. Thus, irrigation is used for agricultural fields, depending on the soil type, and water is provided to the plants. Two essential elements are necessary for farming, the first is the ability to determine the soil’s fertility, and the second is the use of different technologies to reduce the dependence of water on electrical power and on/off schedules. The purpose of this study is to propose a system in which moisture sensors are placed under trees or plants. The gateway unit transmits sensor information to the controller, which then turns on the pump and recycles the water flow. A farmland’s water pump can be remotely controlled and parameters such as moisture and flow rate can be monitored using an HTTP dashboard. In order to evaluate the applicability of IOT-based automatic wastewater irrigation systems, a pilot test was conducted using the developed framework. Theoretically, such a system could be expanded by including any pre-defined selection parameters. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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22 pages, 9083 KiB

Open AccessArticle

A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information

by Antonio Cano-Ortega, Miguel A. García-Cumbreras, Francisco Sánchez-Sutil and Jesús C. Hernández

Electronics 2022, 11(23), 3991; https://doi.org/10.3390/electronics11233991 - 1 Dec 2022

Cited by 3 | Viewed by 1749

Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics [...] Read more.

Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>System architecture.</p> Full article ">Figure 2
<p>Flowchart for the measurement and computation of electric variable: ANR3 board.</p> Full article ">Figure 3
<p>Process timeline for the SM.</p> Full article ">Figure 4
<p>Flowchart for cloud data uploading: WD1m board.</p> Full article ">Figure 5
<p>Hardware block diagram of the SMH.</p> Full article ">Figure 6
<p>Printed circuit board (PCB) of the SMH with battery power supply: (<b>a</b>) front side: (<b>b</b>) back side and (<b>c</b>) assembled with real components.</p> Full article ">Figure 6 Cont.
<p>Printed circuit board (PCB) of the SMH with battery power supply: (<b>a</b>) front side: (<b>b</b>) back side and (<b>c</b>) assembled with real components.</p> Full article ">Figure 7
<p>The framework to deal with electrical consumption in households with big data.</p> Full article ">Figure 8
<p>The device assembled with real components connected in the house.</p> Full article ">Figure 9
<p>General dashboard (<b>a</b>) <span class="html-italic">q</span> and (<b>b</b>) <span class="html-italic">v</span>.</p> Full article ">Figure 10
<p>General dashboard (<b>a</b>) <span class="html-italic">p</span> and (<b>b</b>) <span class="html-italic">i</span>.</p> Full article ">Figure 11
<p>General dashboard (<b>a</b>) <span class="html-italic">PF</span> and (<b>b</b>) s.</p> Full article ">Figure 11 Cont.
<p>General dashboard (<b>a</b>) <span class="html-italic">PF</span> and (<b>b</b>) s.</p> Full article ">Figure 12
<p>Filtering by date.</p> Full article ">Figure 13
<p>Filtering by device.</p> Full article ">Figure 14
<p>General dashboard comparison of (<b>a</b>) q and (<b>b</b>) v values.</p> Full article ">Figure 15
<p>General dashboard comparison of (<b>a</b>) <span class="html-italic">p</span> and (<b>b</b>) <span class="html-italic">i</span> values.</p> Full article ">Figure 15 Cont.
<p>General dashboard comparison of (<b>a</b>) <span class="html-italic">p</span> and (<b>b</b>) <span class="html-italic">i</span> values.</p> Full article ">Figure 16
<p>General dashboard comparison of (<b>a</b>) <span class="html-italic">PF</span> and (<b>b</b>) <span class="html-italic">s</span> values.</p> Full article ">Figure 17
<p>App for non-expert users: (<b>a</b>) house#11 and (<b>b</b>) house#13.</p> Full article ">

18 pages, 1569 KiB

Open AccessArticle

Efficient Communication Model for a Smart Parking System with Multiple Data Consumers

by T. Anusha and M. Pushpalatha

Smart Cities 2022, 5(4), 1536-1553; https://doi.org/10.3390/smartcities5040078 - 2 Nov 2022

Cited by 3 | Viewed by 3707

A smart parking system (SPS) is an integral part of smart cities where Internet of Things (IoT) technology provides many innovative urban digital solutions. It offers hassle-free parking convenience to the city dwellers, metering facilities, and a revenue source for businesses, and it [...] Read more.

A smart parking system (SPS) is an integral part of smart cities where Internet of Things (IoT) technology provides many innovative urban digital solutions. It offers hassle-free parking convenience to the city dwellers, metering facilities, and a revenue source for businesses, and it also protects the environment by cutting down drive-around emissions. The real-time availability information of parking slots and the duration of occupancy are valuable data utilized by multiple sectors such as parking management, charging electric vehicles (EV), car servicing, urban infrastructure planning, traffic regulation, etc. IPv6 wireless mesh networks are a good choice to implement a fail-safe, low-power and Internet protocol (IP)-based secure communication infrastructure for connecting heterogeneous IoT devices. In a smart parking lot, there could be a variety of local IoT devices that consume the occupancy data generated from the parking sensors. For instance, there could be a central parking management system, ticketing booths, display boards showing a count of free slots and color-coded lights indicating visual clues for vacancy. Apart from this, there are remote user applications that access occupancy data from browsers and mobile phones over the Internet. Both the types of data consumers need not collect their inputs from the cloud, as it is beneficial to offer local data within the network. Hence, an SPS with multiple data consumers needs an efficient communication model that provides reliable data transfers among producers and consumers while minimizing the overall energy consumption and data transit time. This paper explores different SPS communication models by varying the number of occupancy data collators, their positions, hybrid power cycles and data aggregation strategies. In addition, it proposes a concise data format for effective data dissemination. Based on the simulation studies, a multi-collator model along with a data superimposition technique is found to be the best for realizing an efficient smart parking system. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>IoT infrastructure in a standalone parking lot.</p> Full article ">Figure 2
<p>Examples of on-site data consumers. (<b>a</b>) Smart bulb. (<b>b</b>) Buzzer for alarm. (<b>c</b>) Ticketing booth. (<b>d</b>) Display screen.</p> Full article ">Figure 3
<p>A display screen placed in a smart parking lot, showing parking availability.</p> Full article ">Figure 4
<p>Mobile application with multiple services. (<b>a</b>) Booking individual parking slot. (<b>b</b>) Searching for available parking slots in a map.</p> Full article ">Figure 5
<p>Over-the-Internet data consumer, a web browser showing parking occupancy.</p> Full article ">Figure 6
<p>Flow of occupancy data (<b>a</b>) BR with one data collator at top. (<b>b</b>) BR with one data collator in middle (<b>c</b>) BR with four distributed data collators (<b>d</b>) BR with four data collators near BR. (<b>e</b>) Routers aggregate occupancy data at each hop.</p> Full article ">Figure 7
<p>Metrics for the communication models. (<b>a</b>) Data reliability in the network (<b>b</b>) Control overhead in the network.</p> Full article ">Figure 8
<p>Metrics for the communication models. (<b>a</b>) Latency for occupancy data packets. (<b>b</b>) Arrival of occupancy data from all nodes.</p> Full article ">Figure 9
<p>Metrics for the communication models. (<b>a</b>) Time to reach local consumers. (<b>b</b>) Percentage of time when radio was active.</p> Full article ">Figure 10
<p>Metrics for the communication models. (<b>a</b>) Average energy utilization of a single node. (<b>b</b>) Battery charge consumption for one hour.</p> Full article ">Figure 11
<p>Performance in a 10 × 10 grid vs. 15 × 15 grid. (<b>a</b>) Data reliability in the network. (<b>b</b>) Control overhead in the network.</p> Full article ">Figure 12
<p>Occpancy data latency in a large network.</p> Full article ">Figure 13
<p>Performance in a 10 × 10 grid vs. 15 × 15 grid (<b>a</b>) Time to reach local consumers. (<b>b</b>) Percentage of time when radio was active.</p> Full article ">

32 pages, 18975 KiB

Open AccessArticle

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

by Muhammad Zuhaib, Faraz Ahmed Shaikh, Wajiha Tanweer, Abdullah M. Alnajim, Saleh Alyahya, Sheroz Khan, Muhammad Usman, Muhammad Islam and Mohammad Kamrul Hasan

Energies 2022, 15(21), 7888; https://doi.org/10.3390/en15217888 - 24 Oct 2022

Cited by 12 | Viewed by 2493

Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, [...] Read more.

Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>High contact resistance (HCR) fault in a MATLAB Simulink environment. A–C shows stator phases.</p> Full article ">Figure 2
<p>Rotor bar fault in MATLAB Simulink Environment A–C stator phases and a–c rotor phases.</p> Full article ">Figure 3
<p>Proposed Algorithm Flowchart (Quality Improved also enlarged-300 dpi).</p> Full article ">Figure 4
<p>Healthy Motor SIMULINK model with A–C stator phases.</p> Full article ">Figure 5
<p>Decomposition procedure for the DWT: representation of the low-pass and high-pass filters, convolving with signal ‘S’. (Figure Reduced in size-300 dpi).</p> Full article ">Figure 6
<p>Representation of DWT Approximation Coefficients and Detail Coefficients of Healthy Phase-A Currents with 2000 samples.</p> Full article ">Figure 7
<p>Representation of DWT approximation coefficients and detail coefficients of HCR fault in Phase-A currents with 2000 samples.</p> Full article ">Figure 8
<p>Representation of DWT approximation coefficients and detail coefficients of a faulty signal rotor bar fault in Phase-A currents with 2000 samples.</p> Full article ">Figure 9
<p>Healthy motor phase: a feature point representation with 1000 samples. (Figures Titled-300 dpi).</p> Full article ">Figure 10
<p>High contact resistance fault in Phase-A: Representation of feature points with 1000 samples. (Figures Titled-300 dpi).</p> Full article ">Figure 11
<p>Rotor bar fault in Phase-A: Representation of feature points with 1000 samples (Figures Titled-300 dpi).</p> Full article ">Figure 12
<p>Neural network (NN) model.</p> Full article ">Figure 13
<p>Neural network model performance parameters and cross-entropy error.</p> Full article ">Figure 14
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for OSS Train Fcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 76.0% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 15
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for OSS TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 82.6% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 16
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for OSS TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 92.2% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 17
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 74.8% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 18
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 93.1% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 19
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 80.0% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 20
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for CGP TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 85.8% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 21
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for CGP TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 91.0% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 21 Cont.
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for CGP TrainFcn/Activation Function at level 9 (RMS) Features for High Contact Resistance Fault with 91.0% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 22
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for OSS TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 83.8% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 23
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for CGP TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 91.3% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 24
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for CGP TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 97.9% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 25
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 84.7% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 26
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 91.3% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">Figure 27
<p>Neural Network Model ROC (Receiver Operating Characteristics) and Confusion Matrix for LM TrainFcn/Activation Function at level 9 (RMS) Features for Rotor Bar Fault with 98.6% Accuracy. Grey line shows diagonal of the ROC for indicating Class 1–6 lines are lying above or below or on it.</p> Full article ">

18 pages, 24416 KiB

Open AccessArticle

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

by Abdelhak Kharbouch, Anass Berouine, Hamza Elkhoukhi, Soukayna Berrabah, Mohamed Bakhouya, Driss El Ouadghiri and Jaafar Gaber

Sensors 2022, 22(20), 7978; https://doi.org/10.3390/s22207978 - 19 Oct 2022

Cited by 13 | Viewed by 3079

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control [...] Read more.

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO₂) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>(<b>a</b>) Energy Efficient Building Lab (EEBLab) test site; (<b>b</b>) Interior side wall of EEBLab with ventilation fan and a set of sensors and other equipment.</p> Full article ">Figure 2
<p>General architecture of an Internet of Things platform.</p> Full article ">Figure 3
<p>The deployed IoT devices; (<b>a</b>) Indoor CO<sub>2</sub>, temperature and humidity node; (<b>b</b>) Inlet and outlet ventilator speed control node; (<b>c</b>) Weather station node for outdoor air quality; (<b>d</b>) Occupants’ number node.</p> Full article ">Figure 4
<p>Data transfer from IoT nodes to the HOLSYS platform via RPi gateways over MQTT and HTTP.</p> Full article ">Figure 5
<p>MQTT stream data flow from/to sensor/actuator/controller.</p> Full article ">Figure 6
<p>General architecture platform for controlling ventilation system based on occupancy forecast and CO<sub>2</sub> measurement.</p> Full article ">Figure 7
<p>Occupants’ numbers over the day in EEBLab.</p> Full article ">Figure 8
<p>The general structure of the MPC framework for the EEBLab ventilation control system.</p> Full article ">Figure 9
<p>MATLAB/Simulink model for ventilation system’s control using MPC.</p> Full article ">Figure 10
<p>The experimental setup architecture of HIL implementation of the MPC for EEBLab ventilation system control enabled by the HOLSYS IoT platform.</p> Full article ">Figure 11
<p>Occupancy forecasting results using LSTM.</p> Full article ">Figure 12
<p>The MPC flow rate output together with the CO<sub>2</sub> concentration variation.</p> Full article ">Figure 13
<p>The ON/OFF flow rate output together with the CO<sub>2</sub> concentration variation.</p> Full article ">Figure 14
<p>Energy consumption variation of the ventilation system during control for both ON/OFF and MPC controllers.</p> Full article ">

19 pages, 974 KiB

Open AccessArticle

Energy-Saving Routing Protocols for Smart Cities

by Douglas de Farias Medeiros, Cleonilson Protasio de Souza, Fabricio Braga Soares de Carvalho and Waslon Terllizzie Araújo Lopes

Energies 2022, 15(19), 7382; https://doi.org/10.3390/en15197382 - 8 Oct 2022

Cited by 13 | Viewed by 2475

In recent decades, expansion in urban areas has faced issues such as management of public waste, noise, mobility, and air quality, among others. In this scenario, Internet of Things (IoT) and Wireless Sensor Network (WSN) scenarios are being considered for Smart Cities solutions [...] Read more.

In recent decades, expansion in urban areas has faced issues such as management of public waste, noise, mobility, and air quality, among others. In this scenario, Internet of Things (IoT) and Wireless Sensor Network (WSN) scenarios are being considered for Smart Cities solutions based on the deployment of wireless remote sensor nodes to monitor large urban areas. However, as the number of nodes increases, the amount of data to be routed increases significantly as well, meaning that the choice of the data routing process has great importance in terms of the energy consumption and lifetime of the network. In this work, we describe and evaluate the energy consumption of routing protocols for WSN-based Smart Cities applications in LoRa-based mesh networks, then propose a novel energy-saving radio power adjustment (RPA) routing protocol. The Cupcarbon network simulator was used to evaluate the performance of different routing protocols in terms of their data package delivery rate, average end-to-end delay, average jitter, throughput, and load consumption of battery charge. Additionally, a novel tool for determining the range of nodes based on the Egli propagation model was designed and integrated into Cupcarbon. The routing protocols used in this work are Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Distance Vector Routing (DVR). Our simulation results show that AODV presents the best overall performance, DSR achieves the best results for power consumption, and DVR is the best protocol in terms of latency. Finally, the proposed RPA routing protocol presents power savings of 11.32% compared to the original DSR protocol. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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<p>Network topology considered in the simulations.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>X</mi> </mrow> </msub> </semantics></math> Adjustment Flowcharts: (<b>a</b>) identification of the <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>T</mi> <mi>X</mi> </mrow> </msub> </semantics></math> level of a destination node and (<b>b</b>) power adjustment before data transmission.</p> Full article ">Figure 3
<p>Power adjustment safety margin versus energy savings.</p> Full article ">Figure 4
<p>Packet delivery rate (PDR) as a function of the source node’s speed for the DVR, AODV, DSR, and Modified DSR protocols.</p> Full article ">Figure 5
<p>End-to-end delay (E2ED) as a function of the source node’s speed for the DVR, AODV, DSR, and Modified DSR protocols.</p> Full article ">Figure 6
<p>Average Jitter (JIT) as a function of the source node’s speed for the DVR, AODV, DSR, and Modified DSR protocols.</p> Full article ">Figure 7
<p>Throughput (THR) as a function of the source node’s speed for the DVR, AODV, DSR, and Modified DSR protocols.</p> Full article ">Figure 8
<p>Total average consumption per protocol.</p> Full article ">

14 pages, 2570 KiB

Open AccessArticle

SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment

by Zeeshan Rasheed, Shahzad Ashraf, Naeem Ahmed Ibupoto, Pinial Khan Butt and Emad Hussen Sadiq

Smart Cities 2022, 5(3), 1115-1128; https://doi.org/10.3390/smartcities5030056 - 5 Sep 2022

Cited by 1 | Viewed by 2520

Communication technologies have drastically increased the number of wireless networks. Heterogeneous networks have now become an indispensable fact while designing the new networks and the way the data packet moves from device to device opens new challenges for transmitting the packet speedily, with [...] Read more.

Communication technologies have drastically increased the number of wireless networks. Heterogeneous networks have now become an indispensable fact while designing the new networks and the way the data packet moves from device to device opens new challenges for transmitting the packet speedily, with maximum throughput and by consuming only confined energy. Therefore, the present study intends to provide a shrewd communication link among all IoT devices that becomes part of numerous heterogeneous networks. The scrumptious dataflow strategy (SDS) for IoT devices in the heterogeneous network environment is proposed and it would deal with all link selection and dataflow challenges. The SDS would accomplish the targeted output in five steps: Step 1 determines the utility rate of each heterogeneous link. Step 2 develops a link selection attribute (LSA) that gauges the loads of network features used for the link selection process. Step 3 calculates the scores of all heterogeneous networks. Step 4 takes the LSA table and computes the network preference for different scenarios, such as round trip time (RTTP), network throughput, and energy consumption. Step 5 sets the priority of heterogeneous networks based on the scores of network attributes. Performance of the proposed SDS mechanism with state of the art network protocols, such as high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR), was determined by conducting a simulation with NS2 and, consequently, the SDS exhibited its shrewd performance. During comparative analysis, in terms of round trip time, the SDS proved that it utilized only 16.4 milliseconds to reach IoT device 50 and was first among all other protocols. Similarly, for network throughput, at IoT device 50, the throughputs of the SDS are recorded at 40% while the rest of other protocols were dead. Finally, while computing the energy consumption used to reach IoT device 50, the SDS was functional and possessed more than half of its energy compared to the other protocols. The SDS only utilized 302 joules while the rest of the protocols were about to die as they had consumed all of their energy. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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25 pages, 4186 KiB

Open AccessArticle

A Prosumer-Oriented, Interoperable, Modular and Secure Smart Home Energy Management System Architecture

by Pedro Gonzalez-Gil, Juan Antonio Martinez and Antonio Skarmeta

Smart Cities 2022, 5(3), 1054-1078; https://doi.org/10.3390/smartcities5030053 - 24 Aug 2022

Cited by 6 | Viewed by 3111

As prices on renewable energy electricity generation and storage technologies decrease, previous standard home energy end-users are also becoming producers (prosumers). Together with the increase of Smart Home automation and the need to manage the energy-related interaction between home energy consumers and Smart [...] Read more.

As prices on renewable energy electricity generation and storage technologies decrease, previous standard home energy end-users are also becoming producers (prosumers). Together with the increase of Smart Home automation and the need to manage the energy-related interaction between home energy consumers and Smart Grid through different Demand Response approaches, home energy management becomes a complex and multi-faceted problem, calling for an extensible, interoperable and secure solution. This work proposes a modular architecture for building a Smart Home Energy Management System, integrable with existing Home Automation Systems, that considers the use of standard interfaces for data communication, the implementation of security measures for the integration of the different components, as well as the use of semantic web technologies to integrate knowledge and build on it. Our proposal is finally validated through implementation in one real smart home test-bed, evaluating the system from a functional standpoint to demonstrate its ability to support our goals. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1

Figure 1
<p>Smart Home energy management.</p> Full article ">Figure 2
<p>Home energy management concerns.</p> Full article ">Figure 3
<p>Knowledge Base-centred architecture of the Smart Home Energy Management System.</p> Full article ">Figure 4
<p>Layered architecture of the Smart Home Energy Management System.</p> Full article ">Figure 5
<p>NGSI-LD Information Model.</p> Full article ">Figure 6
<p>Context Provider information access sequence.</p> Full article ">Figure 7
<p>Context authorisation and access sequence.</p> Full article ">Figure 8
<p>Authentication sequence.</p> Full article ">Figure 9
<p>Authorisation sequence.</p> Full article ">Figure 10
<p>Authorisation enforcement sequence.</p> Full article ">Figure 11
<p>Orchestration asynchronous message passing.</p> Full article ">Figure 12
<p>Test-bed’s Home Assistant installation graphical interface.</p> Full article ">Figure 13
<p>Aerial view of the test-bed location. Pool, PV panels, HVAC and heating units visible.</p> Full article ">Figure 14
<p>Protegé view of PVSystem in DABGEO.</p> Full article ">Figure 15
<p>Secure access of Energy Management Components to the Context Broker.</p> Full article ">Figure 16
<p>Test-bed Energy Management Components and interactions.</p> Full article ">Figure 17
<p>Alerting and notification through Home Assistant.</p> Full article ">Figure 18
<p>Node-RED development of the DER component for PV.</p> Full article ">Figure 19
<p>Power peaks prior to Smart Home Energy Management System.</p> Full article ">Figure 20
<p>Power peaks with Smart Home Energy Management System.</p> Full article ">Figure 21
<p>Grid energy import comparison.</p> Full article ">

18 pages, 5089 KiB

Open AccessArticle

A Monitoring System Based on NB-IoT and BDS/GPS Dual-Mode Positioning

by Zhibo Xie, Ruihua Zhang, Juanni Fang and Liyuan Zheng

Electronics 2022, 11(16), 2493; https://doi.org/10.3390/electronics11162493 - 10 Aug 2022

Cited by 3 | Viewed by 2798

Monitoring system is widely used to detect the environment parameters such as temperature, humidity and position information in cold chain logistic, modern agriculture, hospital and so on. Poor position precision, high communication cost, high packet loss rate are the main problems in current [...] Read more.

Monitoring system is widely used to detect the environment parameters such as temperature, humidity and position information in cold chain logistic, modern agriculture, hospital and so on. Poor position precision, high communication cost, high packet loss rate are the main problems in current monitoring system. To solve these problems, the paper presents a new monitoring system based on Narrow Band Internet of Things (NB-IoT) and BeiDou system/Global System Position (BDS/GPS) dual-mode positioning. Considering the position precision, a dual-mode positioning circuit based on at6558 is designed, and the calculation formula of the positioning information of the monitored target has been derived. Subsequently, a communication network based on wh-nb75-ba NB-IoT module is designed after compared with the LoRa technology. According to the characteristics of high time correlation of sensor data, an adaptive optimal zero suppression (AOZS) compression algorithm is proposed to improve the efficiency of data transmission. Experiments prove the feasibility and effectiveness of the system from the aspects of measurement accuracy, positioning accuracy and communication performance. The temperature and humidity error are less than 1 °C and 5% RH respectively with the selected sensor chips. The position error is decided by several factors, including the number of satellites used for positioning, the monitored target moving speed and NB-IoT module lifetime period. When the monitored target is stationary, the positioning error is about 2 m, which is less than that of the single GPS or BDS mode. When the monitored target moves, the position error will increase. But the error is still less than that of the single GPS or BDS mode. Then the AOZS compression algorithm is used in actually experiment. The compression ratio (CR) of it is about 10% when the data amount increasing. In addition, the packet loss rate test experiment proves the high reliability of the proposed system. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1

Figure 1
<p>Hardware frame of the monitoring system.</p> Full article ">Figure 2
<p>The Sink node physical diagram.</p> Full article ">Figure 3
<p>Sensor node physical diagram.</p> Full article ">Figure 4
<p>Main program flow chart of sink node.</p> Full article ">Figure 5
<p>The Flowchart of NB-IoT subprogram.</p> Full article ">Figure 6
<p>The flowchart of RFID subprogram of sink node.</p> Full article ">Figure 7
<p>The flowchart of BDS/GPS subprogram.</p> Full article ">Figure 8
<p>The flowchart of data compress algorithm.</p> Full article ">Figure 9
<p>The flowchart of sensor nodes.</p> Full article ">Figure 10
<p>The operation flowchart of monitoring center.</p> Full article ">Figure 11
<p>The measurement error. (<b>a</b>) temperature error (<b>b</b>) humidity error.</p> Full article ">Figure 12
<p>The relationship between positioning error and vehicle speed.</p> Full article ">Figure 13
<p>The Comparison of data compression ratio.</p> Full article ">Figure 14
<p>The packet loss rate of our NB-IoT module and LoRa sx1268.</p> Full article ">

19 pages, 991 KiB

Open AccessArticle

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

by Xing Chen and Guizhong Liu

Sensors 2022, 22(13), 4738; https://doi.org/10.3390/s22134738 - 23 Jun 2022

Cited by 25 | Viewed by 3678

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue [...] Read more.

Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1

Figure 1
<p>System Model.</p> Full article ">Figure 2
<p>Convergence property of different algorithm.</p> Full article ">Figure 3
<p>Convergence property of different algorithm.</p> Full article ">Figure 4
<p>Performance evaluation on aggregation interval.</p> Full article ">Figure 5
<p>Performance evaluation on reward.</p> Full article ">Figure 6
<p>Performance evaluation on energy consumption.</p> Full article ">Figure 7
<p>Performance evaluation on reward when the delay threshold is different.</p> Full article ">Figure 8
<p>Delay of different algorithms.</p> Full article ">Figure 9
<p>System bandwidth <span class="html-italic">B</span> = 5 MHz.</p> Full article ">Figure 10
<p>System bandwidth <span class="html-italic">B</span> = 10 MHz.</p> Full article ">

18 pages, 3662 KiB

Open AccessArticle

Research on Energy Saving and Environmental Protection Management Evaluation of Listed Companies in Energy Industry Based on Portfolio Weight Cloud Model

by Shanshan Li, Yujie Wang, Yuannan Zheng, Jichao Geng and Junqi Zhu

Energies 2022, 15(12), 4311; https://doi.org/10.3390/en15124311 - 13 Jun 2022

Cited by 6 | Viewed by 2184

Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry [...] Read more.

Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry comprehensively, this study puts forward the evaluation framework of “governance framework-implementation process-governance effectiveness” for ESEP management level. Based on the comprehensive collection and collating of related information reports (e.g., sustainable development reports) of listed energy companies from 2009 to 2018, the ESEP information was extracted, and the portfolio weight cloud model was used to evaluate the ESEP management status of listed energy companies in China. It is of great theoretical innovation and practical significance to promote the evolution of the economy from “green development” to “dark green development”. The results show that: (1) the number of SHEE information released by listed companies in the energy industry shows a steady increasing trend, but the release rate is low, and there are differentiation characteristics in different industries. (2) The ESEP management level of most listed companies in the energy industry is still at the low level, and only 17.19% (S = 65) of the sample companies are at the level of “IV level-acceptable” and “V level-claimable”. (3) In terms of governance framework-implementation process-governance effectiveness, B1-governance framework (

E_{x}

= 3.4451) and B2-implementation process (

E_{x}

= 2.9480) are relatively high, but B3-governance effectiveness (

E_{x}

= 2.0852) and B4-public welfare (

E_{x}

= 2.0556) are relatively low. The expectation of most ESEP evaluation indexes fluctuates between “III level-transition level” and “II Level-improvement level”. Finally, some suggestions are put forward to improve ESEP management levels. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1
<p>Normal cloud and digital features <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>E</mi> <mi>x</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>E</mi> <mi>n</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mtext> </mtext> <msub> <mi>H</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Schematic diagram of cloud generator. (<b>a</b>) Forward CG. (<b>b</b>) Backward CG. (<b>c</b>) X-conditional CG. (<b>d</b>) Y-conditional CG.</p> Full article ">Figure 3
<p>Quantity distribution of energy saving and environmental protection information disclosure.</p> Full article ">Figure 4
<p>Distribution of energy saving and environmental protection information disclosure.</p> Full article ">Figure 5
<p>Comprehensive membership evaluation results of each company.</p> Full article ">Figure 6
<p>Evaluation cloud map of target layer and criterion layer. (<b>a</b>) Evaluation grade cloud scale; (<b>b</b>) ESEP comprehensive evaluation cloud chart.</p> Full article ">Figure 7
<p>Evaluation cloud chart. (<b>a</b>) ESEP governance framework; (<b>b</b>) ESEP implementation process; (<b>c</b>) ESEP governance effectiveness; (<b>d</b>) ESEP charity and others.</p> Full article ">Figure 8
<p>Expected value of each index cloud model.</p> Full article ">

16 pages, 2947 KiB

Open AccessArticle

Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning

by Yuan Shi and Xianze Xu

Sensors 2022, 22(9), 3264; https://doi.org/10.3390/s22093264 - 24 Apr 2022

Cited by 30 | Viewed by 3299

Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns raised by supervision departments and users limit the data for sharing. Meanwhile, the limited [...] Read more.

Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns raised by supervision departments and users limit the data for sharing. Meanwhile, the limited data from the newly built houses are not sufficient to support building a powerful model. Another problem is that the data from different houses are in a non-identical and independent distribution (non-IID), which makes the general model fail in predicting accurate load for the specific house. Even though we can build a model corresponding to each house, it costs a large computation time. We first propose a federated transfer learning approach applied in STLF, deep federated adaptation (DFA), to deal with the aforementioned problems. This approach adopts the federated learning architecture to train a global model without undermining privacy, and then the model leverage multiple kernel variant of maximum mean discrepancies (MK-MMD) to fine-tune the global model, which makes the model adapted to the specific house’s prediction task. Experimental results on the real residential datasets show that DFA has the best forecasting performance compared with other baseline models and the federated architecture of DFA has a remarkable superiority in computation time. The framework of DFA is extended with alternative transfer learning methods and all of them achieve good performances on STLF. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1
<p>A horizontal federated learning architecture.</p> Full article ">Figure 2
<p>Overview of the deep federated adaptation. The top box is the master server while the 3 bottom boxes denotes 3 houses. Each house contains one computing device connected to the master server for processing the data. The data collected by the smart meter is locked and cannot be transmitted to the master server.</p> Full article ">Figure 3
<p>The architecture of proposed network, from top to bottom, consists of CNN layers, BiLSTM layers and fully connected layers.</p> Full article ">Figure 4
<p>Load data of four houses for one day from the used datasets.</p> Full article ">Figure 5
<p>MAPE values of DFA and four baseline models for 10 houses.</p> Full article ">Figure 6
<p>MAPE values of four baseline models and DFA with different numbers of houses connected to the federated system for 10 houses.</p> Full article ">Figure 7
<p>Correlation between accuracy and number of rounds for the federated and centralized architecture.</p> Full article ">Figure 8
<p>Ablation experiments of the federated architecture and MK-MMD optimization on 5 houses.</p> Full article ">Figure 9
<p>Extensibility experiments with alternative transfer learning methods on 5 houses.</p> Full article ">

21 pages, 2422 KiB

Open AccessArticle

EggBlock: Design and Implementation of Solar Energy Generation and Trading Platform in Edge-Based IoT Systems with Blockchain

by Subin Kwak, Joohyung Lee, Jangkyum Kim and Hyeontaek Oh

Sensors 2022, 22(6), 2410; https://doi.org/10.3390/s22062410 - 21 Mar 2022

Cited by 7 | Viewed by 3334

In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can [...] Read more.

In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum’s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1

Figure 1
<p>System model.</p> Full article ">Figure 2
<p>Auction-based energy transaction model.</p> Full article ">Figure 3
<p>Ethereum architecture.</p> Full article ">Figure 4
<p>Sequence diagram for the energy trading.</p> Full article ">Figure 5
<p>Blueprint of controllers in testbed.</p> Full article ">Figure 6
<p>Testbed of platform.</p> Full article ">Figure 7
<p>Process of energy trading in web page. (<b>a</b>) Real time user’s information; (<b>b</b>) Purchase section; (<b>c</b>) Purchase progress pop-up window; (<b>d</b>) Ethereum transaction preview; (<b>e</b>) Transaction in progress; (<b>f</b>) Transaction completion.</p> Full article ">Figure 7 Cont.
<p>Process of energy trading in web page. (<b>a</b>) Real time user’s information; (<b>b</b>) Purchase section; (<b>c</b>) Purchase progress pop-up window; (<b>d</b>) Ethereum transaction preview; (<b>e</b>) Transaction in progress; (<b>f</b>) Transaction completion.</p> Full article ">Figure 7 Cont.
<p>Process of energy trading in web page. (<b>a</b>) Real time user’s information; (<b>b</b>) Purchase section; (<b>c</b>) Purchase progress pop-up window; (<b>d</b>) Ethereum transaction preview; (<b>e</b>) Transaction in progress; (<b>f</b>) Transaction completion.</p> Full article ">Figure 8
<p>Provided information in mobile application. (<b>a</b>) Main page; (<b>b</b>) Contract address; (<b>c</b>) Market price; (<b>d</b>) Price with volume; (<b>e</b>) Account lookup; (<b>f</b>) Transaction history.</p> Full article ">Figure 8 Cont.
<p>Provided information in mobile application. (<b>a</b>) Main page; (<b>b</b>) Contract address; (<b>c</b>) Market price; (<b>d</b>) Price with volume; (<b>e</b>) Account lookup; (<b>f</b>) Transaction history.</p> Full article ">Figure 9
<p>Adjusting parameters according to the amount of solar energy generated per day. (<b>a</b>) Adjusting number of episode; (<b>b</b>) Adjusting learning rate; (<b>c</b>) Adjusting discount factor.</p> Full article ">Figure 10
<p>Simulation result. (<b>a</b>) Compared with static system; (<b>b</b>) Compared with regular system; (<b>c</b>) Compared with heuristic system; (<b>d</b>) Total amount of generated energy.</p> Full article ">Figure 11
<p>Determination of transaction ratio of seller according to environmental changes. (<b>a</b>) Compare with static system; (<b>b</b>) Compare with static system.</p> Full article ">Figure 12
<p>Determination of bidding cost of buyer <span class="html-italic">i</span> according to environmental changes. (<b>a</b>) Compared with static system; (<b>b</b>) Compared with static system.</p> Full article ">Figure 13
<p>Energy trading test.</p> Full article ">

16 pages, 3445 KiB

Open AccessArticle

LPSRS: Low-Power Multi-Hop Synchronization Based on Reference Node Scheduling for Internet of Things

by Mahmoud Elsharief, Mohamed A. Abd El-Gawad, Haneul Ko and Sangheon Pack

Energies 2022, 15(6), 2289; https://doi.org/10.3390/en15062289 - 21 Mar 2022

Cited by 5 | Viewed by 2191

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in [...] Read more.

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively. Full article

(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)

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Figure 1
<p>Scheduling process’s flow chart.</p> Full article ">Figure 2
<p>Sensor network example.</p> Full article ">Figure 3
<p>The tree created by the LPSRS scheduling process.</p> Full article ">Figure 4
<p>The synchronization process of LPSRS in a single broadcast domain. <math display="inline"><semantics> <mi>R</mi> </semantics></math> is the reference node, <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>I</mi> <mi>R</mi> </mrow> </semantics></math> is the specified reference node, and <math display="inline"><semantics> <mi>K</mi> </semantics></math> represents the other neighbor’s nodes of <math display="inline"><semantics> <mi>R</mi> </semantics></math>.</p> Full article ">Figure 5
<p>The 25-node setup.</p> Full article ">Figure 6
<p>The 3-way grid network.</p> Full article ">Figure 7
<p>Required number of messages (<span class="html-italic">r</span> = 85 m, N = 240: 450).</p> Full article ">Figure 8
<p>Required number of messages (<span class="html-italic">r</span> = 85 m: 160 m, N = 240).</p> Full article ">

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000
IoT IoT	-	8.5	2020	15.9 Days	CHF 1200

Submit your Abstract

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000
IoT IoT	-	8.5	2020	15.9 Days	CHF 1200

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