Improving smart grid security through 5G enabled IoT and edge computing

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

As a result of the old-style electric grid's inability to handle the extensive incorporation of renewable energy resources and distributed renewable energy resources into the electric grid. In the twenty-first century, smart grid technology has entered the picture. Because smart grids operate on two-way communication, communication and information technology are critical to the smart grid's effective operation. The advanced version of the smart grid structure is presented in this article. Furthermore, this article discusses existing and advanced communication and information technology. Finally, the manuscript discusses future research advancements in communication and the most crucial information technologies, such as the Internet of Things (IoT), cloud computing, fog and edge computing, and blockchain in the smart grid. So, the smart grid can manage the massive integration of renewable energy resources while providing customers with efficient, sustainable, and reliable power.KeywordsIoT-Internet of ThingICT-Information and Communication TechnologyCC: Cloud Computing

The Internet of Things (IoT) has transpired as a fascinating technology for smart cities, smart homes, and smart grids by using a vast amount of IoT data. A smart grid is one of the core components where transport, generation, delivery, and electricity consumption are enhanced in terms of protection and reliability. The existing power grid is suffering from many problems such as outages and unpredictable power disturbances, inflexible energy rates, unnoticeable customer fraud, and many other disadvantages. These problems lead to the ever-rising demand for fossil fuel and service costs. For example, the peak hour demand needs to be overestimated and more energy generated to minimize the risk of an outage. The main problem of the smart grid is the tremendous amount of data needs to be collected from the IoT devices, and processing the data is a challenge. Using and predicting a large amount of data in smart Grid and IoT is still in its infancy. To remedy this problem, we propose a hybrid solution by using the Cloud and Edge Computing to process the data. Processing and predicting at the edge that is close to the embedded devices and homes to save in latency and storage compared to putting all the processing in the Cloud. In this paper, we define a hybrid solution where we use the edge computing for the smart grid information processing where the microgrids are located on the edge of the IoT network, and on the Cloud to use for the power grid that distributes power to the microgrids. We proposed a machine learning engine that used the decision tree to establish the communication between the edge layer, failover between edges, and the Cloud layer.

Smart grid applications typically use cloud computing to address the computational requirements for efficient electricity delivery. Recently, the emerging Internet of Things (IoT) has resulted in increased number of devices connected to the smart grid, including consumer gadgets, measurement equipment, and electrical and electronic devices such as smart power converters, phasor measurement units, and smart meters. These heterogeneous devices that are present in all the four stages of a smart grid—generation, transmission, distribution, and consumption—generate huge amount of structured, semi structured and unstructured data. Gathering, storing, and processing such huge data volumes using cloud computing creates problems of bandwidth, latency, disaster recovery, and cost. To overcome these problems, we present a theoretical discussion on the usage of IoT, edge computing and big data to collect the data from smart grid, process the data using edge computing and big-data analytics, and use the data for smart grid maintenance, energy information and future decisions. Further, we highlight how edge computing and big-data approaches have mitigated the above mentioned problems by shifting the control, intelligence, and trust to the edge of the network.

In smart power grid, communication network is an integral fragment that communicate vital information to various stakeholders for efficient transmission of electricity. For autonomous functioning of power system, the sensor node connected to grid components enable information sharing for monitoring and control of the state of the grid. Also, the smart grid deployment scenarios require data communication to various grid nodes to be reliable, secure and standards compliant. In this paper, we have proposed a conceptual architecture for evaluating IoT deployment scenario and presenting power system simulation on real-time simulator and experimental evaluation of IPv6 based IETF 6TiSCH compliant grid nodes in smart grid scenario. The proposed architecture for simulation and experiments leverages Internet of Things (IoT) approach to address the smart grid requirement.

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Internet of Things (IoT) is a connection of people and things at any time, in any place, with anyone and anything, using any network and any service. Thus, IoT is a huge dynamic global network infrastructure of Internet-enabled entities with web services. One of the most important applications of IoT is the Smart Grid (SG). SG is a data communications network which is integrated with the power grid to collect and analyze data that are acquired from transmission lines, distribution substations, and consumers. In this paper, we talk about IoT and SG and their relationship. Some IoT architectures in SG, requirements for using IoT in SG, IoT applications and services in SG, and challenges and future work are discussed.

We present communication frameworks, models, and protocols of smart grid Internet of Things (IoT) networks based on the IEEE and IEC standards. The measurement, control, and monitoring of grid being achieved through phasor measurement unit (PMU) based wide area measurement (WAM) framework. The WAM framework applied the IEEE standard C37.118 phasor exchange protocol to collect grid data from various substation devices. The existing frameworks include the IEC 61850 protocol and programmable logic controllers (PLCs) based supervisory control and data acquisition (SCADA) system. These protocols have been selected as per the smart grid configuration and communication design. However, the existing frameworks have severe synchronization errors due to the communication delays of IoT networks in the smart grid. Therefore, this article designs the timing mechanism and a delay model to reduce the timing delay and boost real‐time measurement, monitoring, and control performance of the smart grid WAM applications. The result shows that the proposed model outperformed the existing WAM system.

The “Internet of Things (IoT)” is just the next phase in the expansion of the internet, in which any physical article/aspect with computation and correspondence skills may want to be flawlessly coordinated, to the Internet on a multitude of levels. “The Smart Grid, which is considered to be perhaps the most fundamental infrastructure, is characterized as the historical trend electrical energy grid accelerated with a huge scope “ICT” and environmentally friendly electricity incorporation, must be regarded as one of the largest IoT companies”. Nonetheless, security is viewed as one of the primary challenges impeding the rapid and wide-ranging receipt and transmission of both the IoT vision as well as the Smart Grid. In this article, we look at the security issues and challenges on the “IoT"-based smart grid, as well as the large-scale security benefits that we should consider while regulating smart grid privacy. The electrical companies as of now have records recuperation capacities with understand to their checking and control, yet these are as but restricted. The gathering of “IoT” and Smart Grids opens the way to facts assortment from all marks of the corporation and continuously. The smart grid is an employer for the automobile and appropriation of electricity that is upgraded by way of way of limits as some distance as advanced control, reconnaissance, and telecommunications. Digital trades are strengthening (creation, circulation, and utilization are interconnected) and the utilization of related objects (working with far flung checking, understanding, and control of the whole well well worth chain) builds the degree of weak point of statistics systems industrial. The reason for this work is to introduce a complete outline of difficulties, risks, and a few arrangements of protection in “IoT” principally based smart grid.

Internet-of-Things (IoT) as an emerging computing paradigm, refers to interconnected devices with sensing, processing and communication capabilities that promise to offer new intelligent services in many aspects of urban life, such as smart homes, smart cities, smart transportation systems and smart energy. One of the key components of smart energy systems, smart grids, utilize IoT technology in order to exchange data between different devices in the network. In this chapter, we propose an Edge-Computing based methodology that targets to balance the content generation of cameras in an IoT environment deployed for surveillance of a smart grid infrastructure. The purpose of the methodology is to allow smart grid to incorporate devices that generate visual content in the existing infrastructure by meeting the applications’ requirements, efficiently utilizing the available resources and achieve the highest Quality-of-Service.

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.

The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.