Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Smart grid technologies and applications

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Communication network requirements for major smart grid applications in HAN, NAN and WAN

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

Introduction to the Special Issue on edge intelligence: Neurocomputing meets edge computing

Smart Surveillance Systems with Edge Intelligence: Convergence of Deep Learning and Edge Computing

In recent years, there is development in the field of computing devices; Internet of Things (IoT) becomes the latest trend. IoT comprises ubiquitous things that are associated with day-to-day life of individuals like smartphones, smart TV, laptops, and now vehicles too. Internet of Vehicles (IoV) has become the latest area of research used to develop applications in the field of traffic management and road safety. A collaborative approach of cloud and edge computing is termed cloud-edge computing. To manage the enormous amount of IoT devices and the coordination among IoT, the cloud and edge concept of computation offloading is required. In the process of computation offloading, tasks are computationally offloaded to the cloud data center that enhances the resource utilization of the cloud server and minimizes the energy utilization for the tasks. This paper represents the literature review related to various computational offloading schemes in cloud-edge computing proposed as part of the study. The resources comprise of related book chapters and research papers from different publishers of international and national reputes. The study is carried out with the analysis of various computation offloading schemes in cloud-edge computing for the Internet of Vehicles. In addition, computing technologies like cloud computing, edge computing, and computation offloading for the Internet of Vehicles (IoV) were also discussed.KeywordsInternet of Things (IoT)Edge computingCloud computingComputation offloadingInternet of Vehicles (IoV)

With the advent of new technologies and the fast pace of human life, patients today require a sophisticated and advanced smart healthcare framework that is tailored to suit their individual health requirements. Along with 5G and state-of-the-art smart Internet of Things (IoT) sensors, edge computing provides intelligent, real-time healthcare solutions that satisfy energy consumption and latency criteria. Earlier surveys on smart healthcare systems were centered on cloud and fog computing architectures, security, and authentication, and the types of sensors and devices used in edge computing frameworks. They did not focus on the healthcare IoT applications deployed within edge computing architectures. The first purpose of this study is to analyze the existing and evolving edge computing architectures and techniques for smart healthcare and recognize the demands and challenges of different application scenarios. We examine edge intelligence that targets health data classification with the tracking and identification of vital signs using state-of-the-art deep learning techniques. This study also presents a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence. Even with its many advantages, edge intelligence poses challenges related to computational complexity and security. To offer a higher quality of life to patients, potential research recommendations for improving edge computing services for healthcare are identified in this study. This study also offers a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare.

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions.

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.

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.

Intelligent task prediction and computation offloading based on mobile-edge cloud computing

This article investigates and analyzes the security aspects of 5G specifications from the perspective of IoT‐based smart grids. As the smart grid requires high‐speed and reliable communication to enable real‐time grid monitoring via Internet of Things (IoT) devices, 5G can be considered a catalyst to transform the current power grid infrastructure into a smart grid. Thus, an understanding of what 5G can bring in terms of cyber security in IoT‐based smart grids is important for design decisions and future risk analysis efforts. In this article, we explore a smart grid use case on automatic voltage control—a use case utilizing 5G as a wireless communication infrastructure with edge support. We identify the benefits 5G brings to several security aspects, and show how 5G security techniques are applicable to the smart grid, thus providing a foundation for future security analysis of 5G enabled smart grid systems. Future research should extend this work to additional smart grid use cases.

In general, one of the most important goals of smart grids; the establishment of networks that allow for more distributed generation and energy storage at the end-user side. Energy storage technology; It has great potential to improve electric power systems, increase renewable electricity generation and offer alternatives to petroleum-derived fuels in the transportation sector. This study sheds light on the advantages offered by energy storage technologies, which play an active role in solving the problems encountered during the grid integration of renewable energy sources in smart grid applications. In addition, different solutions offered by different energy storage technologies such as Vanadium are also evaluated in this study. In this study, an energy storage unit with high energy and high power density was designed with the Hybrid Energy Storage System (V-HEES) consisting of vanadium and ultra-capacitor. In addition, the results of the hybrid energy storage system consisting of vanadium and ultra-capacitors and the application areas of these technologies in smart grids are also included.

The integration of blockchain technology into smart grids presents a transformative solution to address the growing concerns surrounding energy data security, transaction transparency, and decentralized energy management. This chapter explores the pivotal role of blockchain in enhancing the functionality and resilience of smart grids, particularly in the context of renewable energy transactions. By leveraging blockchain’s decentralized nature, immutable ledger, and cryptographic security, smart grids can secure data transmission, ensure transparent peer-to-peer (P2P) energy trading, and foster trust among consumers and energy producers. The chapter delves into various case studies that highlight the successful implementation of blockchain as a security layer, demonstrating its potential to mitigate cyber threats, prevent data manipulation, and improve overall grid efficiency. The intersection of blockchain with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is examined, providing a comprehensive outlook on future developments in smart grid ecosystems. Key challenges, opportunities, and the scalability of blockchain solutions for large-scale energy systems are critically assessed. This chapter serves as a timely resource for researchers, policymakers, and industry stakeholders, offering valuable insights into the adoption and impact of blockchain in the renewable energy sector.