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Special Issue "IoT for Smart Grids: Challenges, Opportunities and Trends"

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

Deadline for manuscript submissions: closed (15 October 2021).

Special Issue Editors

Dr. Haris Pervaiz
E-Mail Website
Guest Editor
School of Computing and Communications, Lancaster University, Lancaster, UK
Interests: energy-efficient green communications and networking; 5G/6G networks; intelligent communication techniques; smart grids communication; vehicular networks and machine learning
Dr. Muhammad Ismail
E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Engineering, Tennessee Tech University, Cookeville, TN, USA
Interests: smart grids; networking; cyber-physical security; blockchain; resource allocation; machine learning; optimization; stochastic modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Islam Safak Bayram
E-Mail Website
Guest Editor
Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK
Interests: electric vehicle grid integration; demand-side management; energy storage systems; sustainability; stochastic networks; optimization and control
Special Issues, Collections and Topics in MDPI journals
Dr. Sukhpal Singh Gill
E-Mail Website
Guest Editor
Affiliation: School of Electronic Engineering & Computer Science, Queen Mary University of London, London, UK
Interests: cloud computing; fog computing; software engineering; Internet of things and healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in communication systems and the emergence of the Internet of things (IoT) hold a promise to transform the electrical power grids into smart grids that host higher shares of renewable energy generation, deploy energy storage technologies, charge large collections of electric vehicles, and enable demand response at customer sites. In this context, the IoT technology increases the efficiency and sustainability of electricity generation, delivery, and use. Specifically, IoT-enabled devices provide operational intelligence and deliver insights to the grid operators, paving the way to dynamic control and management of the grid assets, reduction of maintenance cost, and increase of operational safety. Early IoT applications include smart home energy management, power system state estimation, and forecasting of asset failures before leading to blackouts, while there is a growing amount of interest to employ IoT technology in other smart grid applications including electric vehicle load management, inverter control for PV systems, estimation of state-of-charge of energy storage units, and microgrid control.


Deploying IoT networks creates torrents of data, requiring advanced analytics, computation, and machine learning tools to uncover new insights related to electricity generation, transmission, distribution, and use. Furthermore, cybersecurity and privacy are growing concerns, as cyber-attacks could lead to catastrophic consequences with long-lasting ramifications. This Special Issue aims to provide an ideal venue to make innovative contributions to IoT for smart grids, including novel network architectures and joint optimization of power and communication networks for efficient planning and operation. We invite experimental, simulation-based, and/or analytical research with well-elaborated realistic case studies.

We look forward to receiving your submission on the topic of “IoT for Smart Grids: Challenges, Opportunities, and Trends”. The topics of interest for this Special Issue include, but are not limited to:

  • False data injection attack and detection in smart grid IoT;
  • Data analytics in smart grid IoT;
  • Fog/edge/cloud-based service solutions for smart grid IoT;
  • Machine learning and deep learning for resilient and efficient smart grid IoT;
  • Security threats and vulnerability detection in smart grid IoT;
  • Energy theft detection in modern smart grid IoT;
  • Energy efficient deployments for smart grid IoT;
  • Data-driven framework for energy theft detection in distributed renewable energy resources (DRES) using smart grid IoT;
  • Big data analytics in smart grid IoT;
  • Intrusion detection in smart grid IoT;
  • Privacy and security issues in AI applications in smart grid IoT;
  • Secure integration of IoT solutions to smart grids;
  • Privacy and security issues in smart grid IoT interoperability;
  • Game theoretic study of smart grid IoT security and privacy problems;
  • Information theoretic models of privacy and security in smart grid IoT;
  • Data mining and machine learning algorithms for smart charging/discharging of EVs using smart grid IoT;
  • Frameworks, roadmaps, or mechanisms for integrating the smart power grid and intelligent transportation systems using smart grid IoT;
  • Advanced metering infrastructures in smart grid IoT;
  • Benchmarking machine learning models for smart grid IoT communications;
  • Big data, IoT, and machine learning for resilient smart grid infrastructure;
  • Integration of secure solutions for industrial IoT and internet of energy;
  • Security, interoperability, and design models for smart grid IoT using deep learning models;
  • Robustness and fault-tolerance in smart grid IoT using deep learning models;
  • Privacy preserving data aggregation and protection using deep learning models in smart grid IoT;
  • Security and privacy issues in fog/edge-enabled models for smart grid IoT;
  • Economics and performance analysis of smart grid IoT using deep learning models.

Dr Haris Pervaiz
Dr Muhammad Ismail
Dr Islam Safak Bayram
Dr Sukhpal Singh Gill
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Article
Impact of Compression and Small Cell Deployment on NB-IoT Devices Coverage and Energy Consumption with a Realistic Simulation Model
Sensors 2021, 21(19), 6534; https://doi.org/10.3390/s21196534 - 30 Sep 2021
Viewed by 363
Abstract
In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a [...] Read more.
In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a realistic correlated shadow-fading map and path-loss calculation for the environment. Our analysis shows significant reductions in the number of MTC devices in outage by deploying either small cells or Device-to-Device (D2D) communications. In addition, we analyzed the energy consumption of the MTC devices for different data packet sizes and Maximum Coupling Loss (MCL) values. Finally, we study how compression techniques can extend the battery lifetime of MTC devices. Full article
(This article belongs to the Special Issue IoT for Smart Grids: Challenges, Opportunities and Trends)
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Article
Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure
Sensors 2021, 21(6), 1935; https://doi.org/10.3390/s21061935 - 10 Mar 2021
Cited by 2 | Viewed by 630
Abstract
This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail [...] Read more.
This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate. Full article
(This article belongs to the Special Issue IoT for Smart Grids: Challenges, Opportunities and Trends)
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Article
Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework
Sensors 2021, 21(3), 1016; https://doi.org/10.3390/s21031016 - 02 Feb 2021
Cited by 2 | Viewed by 811
Abstract
In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, [...] Read more.
In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme. Full article
(This article belongs to the Special Issue IoT for Smart Grids: Challenges, Opportunities and Trends)
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