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Wireless Sensor Networks in Smart Grid Communications
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Wireless Sensor Networks in Smart Grid Communications

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8168

Special Issue Editors

Dr. Yuanfang Chen

E-Mail Website
Guest Editor
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China
Interests: artificial intelligence; artificial intelligence of things; cyber security; industrial internet of things; algorithm design
Dr. Muhammad Alam

E-Mail Website
Co-Guest Editor
School of Engineering, London South Bank University, London SE1 0AA, UK
Interests: artificial intelligence; cyber security; wireless sensor networks
Prof. Dr. Xiaohua Xu

E-Mail Website
Co-Guest Editor
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Interests: edge computing, artificial intelligence of things, Quantum AI
Prof. Dr. Sardar M.N. Islam

E-Mail Website
Co-Guest Editor
Institute for Sustainable Industries & Livable Cities, Victoria University, Melbourne 14428, Australia
Interests: IoT; Artificial Intelligence; Machine Learning; Deep Learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qiuhua Zheng

E-Mail Website
Co-Guest Editor
School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: industrial internet security; active defense; web security; big data security
Dr. Jia Liu

E-Mail Website
Co-Guest Editor
School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: machine learning; artificial intelligence; variational inference; cyber security; wireless sensor networks

Special Issue Information

Dear Colleagues,

The smart grid is an emerging technology that is supporting significant changes in economies and social networks worldwide. In the energy sector, various regional and state grid systems have begun the transformation from being consumers of electricity to producing, sharing, and storing energy by deploying smart grid infrastructure. However, due to often remote and extreme conditions, in smart grids, cybersecurity and resilience are two of the many challenges in leveraging energy grids. Considering hackers’ recent attacks on energy grids and the distributed structure of these systems, the use of traditional means of computer protection and searching for crimes becomes more difficult or insufficient. Luckily, with the aid of state-of-the-art cybersecurity technologies and resilient systems, we can produce a feasible scheme to ensure the security of smart grids from the source. Hence, there is a need to study and design new cybersecurity technologies and resilient systems for smart grids.

A draft version of CFP:

The purpose of this Special Issue is to bring together researchers from various areas, all working on solving the problems of smart grid cybersecurity and resilience. The aim is to discuss: (i) the recently developed machine learning and data mining techniques that can be used to address the challenges of IIoT security; and (ii) the practical research directions of IIoT security in the machine learning and data mining community.

Topics of interest include, but are not limited to:

  • Algorithms, models, and theories of data mining, including big data mining, to solve the smart grid security problems.
  • Machine learning and statistical methods for data mining in the domain of smart grid security.
  • Mining from the heterogeneous data sources of industrial environment, including spatio-temporal, time-series, streaming, graph, and multimedia data.
  • Resilient and robust systems and platforms for smart grid security, and their efficiency, scalability, security, and privacy.
  • The analysis and design of smart grid resilience.
  • Data mining for modeling and visualizing a smart grid security problem.
  • Decision-making and problem-solving networks in smart grids.
  • Emerging data-mining-based applications in smart grid security.
  • Security issues and solutions for smart grid networks.
  • Lightweight encryption and decryption algorithms that ensure smart grid network security.
  • Hardware design of smart grids involving security chips.
  • Architectures and algorithms for smart grids.
  • Automatic learning techniques in smart grid security systems and smart grid networks.

We particularly encourage submissions in emerging topics of high importance such as machine learning, deep learning, big data mining and analytics, smart grid systems, and heterogeneous data integration and mining.

Dr. Yuanfang Chen
Dr. Muhammad Alam
Prof. Dr. Xiaohua Xu
Prof. Dr. Sardar M.N. Islam
Dr. Qiuhua Zheng
Dr. Jia Liu
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 submissions that pass pre-check are 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 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

35 pages, 4576 KiB  
Article
Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
by Marcos Alberto Mochinski, Marina Luísa de Souza Carrasco Vieira, Mauricio Biczkowski, Ivan Jorge Chueiri, Edgar Jamhour, Voldi Costa Zambenedetti, Marcelo Eduardo Pellenz and Fabrício Enembreck
Sensors 2022, 22(23), 9105; https://doi.org/10.3390/s22239105 - 23 Nov 2022
Cited by 5 | Viewed by 2030
Abstract
In a smart grid communication network, positioning key devices (routers and gateways) is an NP-Hard problem as the number of candidate topologies grows exponentially according to the number of poles and smart meters. The different terrain profiles impose distinct communication losses between a [...] Read more.
In a smart grid communication network, positioning key devices (routers and gateways) is an NP-Hard problem as the number of candidate topologies grows exponentially according to the number of poles and smart meters. The different terrain profiles impose distinct communication losses between a smart meter and a key device position. Additionally, the communication topology must consider the position of previously installed distribution automation devices (DAs) to support the power grid remote operation. We introduce the heuristic method AIDA (AI-driven AMI network planning with DA-based information and a link-specific propagation model) to evaluate the connectivity condition between the meters and key devices. It also uses the link-received power calculated for the edges of a Minimum Spanning Tree to propose a simplified multihop analysis. The AIDA method proposes a balance between complexity and efficiency, eliminating the need for empirical terrain characterization. Using a spanning tree to characterize the connectivity topology between meters and routers, we suggest a heuristic approach capable of alleviating complexity and facilitating scalability. In our research, the interest is in proposing a method for positioning communication devices that presents a good trade-off between network coverage and the number of communication devices. The existing literature explores the theme by presenting different techniques for ideal device placement. Still rare are the references that meticulously explore real large-scale scenarios or the communication feasibility between meters and key devices, considering the detailed topography between the devices. The main contributions of this work include: (1) The presentation of an efficient AMI planning method with a large-scale focus; (2) The use of a propagation model that does not depend on an empirical terrain classification; and (3) The use of a heuristic approach based on a spanning tree, capable of evaluating a smaller number of connections and, even so, proposing a topology that uses fewer router and gateway positions compared to an approach that makes general terrain classification. Experiments in four real large-scale scenarios, totaling over 230,000 smart meters, demonstrate that AIDA can efficiently provide high-quality connectivity demanding a reduced number of devices. Additional experiments comparing AIDA’s detailed terrain-based propagation model to the Erceg-SUI Path Loss model suggest that AIDA can reach the smart meter’s coverage with a fewer router positions. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Grid Communications)
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24 pages, 1102 KiB  
Article
Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System
by Xianjie Zhang, Yu Liu, Wenjun Li and Chen Gong
Sensors 2022, 22(20), 7785; https://doi.org/10.3390/s22207785 - 13 Oct 2022
Cited by 2 | Viewed by 1645
Abstract
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power [...] Read more.
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Grid Communications)
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16 pages, 1800 KiB  
Article
A Hierarchical Network with Fault Tolerance by a Multi-Factor Method for Neighborhood Area Network in Smart Grid
by Jiatao Du, Xiaohui Li and Jie He
Sensors 2022, 22(16), 6218; https://doi.org/10.3390/s22166218 - 19 Aug 2022
Viewed by 1536
Abstract
The neighborhood area network of a smart grid usually has hierarchical wireless communication. Due to forwarding and processing more data, the upper-layer nodes are more likely to suffer congestion and energy exhaustion. This phenomenon leads to the failure of uploading data to the [...] Read more.
The neighborhood area network of a smart grid usually has hierarchical wireless communication. Due to forwarding and processing more data, the upper-layer nodes are more likely to suffer congestion and energy exhaustion. This phenomenon leads to the failure of uploading data to the control center. To solve this problem, this paper proposes a scheme for constructing a multi-factor fault-tolerant hierarchical network. This scheme firstly defines a criterion for the generation of redundant links by multi-factor method in a hierarchical wireless network with the characteristics of the neighborhood area network. Then the redundant links are used to reconstruct the existing topology of the neighborhood area network for improving fault tolerance. Finally, a greedy routing algorithm is put forward to select a proper data transmission path by bypassing low energy nodes, further reducing the failure of uploading data to the control center. The simulation results show that the proposed scheme can effectively improve the fault tolerance of the network topology of the wireless neighborhood area network and balance the network energy consumption. Compared with the original scheme, the proposed scheme improves the fault tolerance by 35% and the relative transmission rate by 21%. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Grid Communications)
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23 pages, 4784 KiB  
Article
Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
by Babak Arbab-Zavar, Suleiman M. Sharkh, Emilio J. Palacios-Garcia, Juan C. Vasquez and Josep M. Guerrero
Sensors 2022, 22(16), 6006; https://doi.org/10.3390/s22166006 - 11 Aug 2022
Cited by 3 | Viewed by 1587
Abstract
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on [...] Read more.
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Grid Communications)
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