Smart grid cyber-physical systems (SG-CPS) are intelligent platforms that incorporate IoT-enabled multifunctional layers including the physical, perception, communication, cyber, and application layers. It includes supervisory control and data acquisition, wide-area measurement systems, and advanced metering infrastructure for remote data aggregation, monitoring, and control operations. From an environmental perspective, these green technologies support two-way operations, which generate and transmit data over wired and wireless communication systems. However, this critical infrastructure faces data privacy and cybersecurity challenges. Hence, extensive research is required to address data privacy and security gaps to strengthen national grid cybersecurity and reduce economic losses. Therefore, this review highlights cryptographic techniques as significant mechanisms to ensure data confidentiality, integrity, and availability. Accordingly, we investigate IoT-enabled multifunctional layer components and applications, their cybersecurity objectives, requirements, and essential cryptographic standards, protocols, and cyber-attacks. Subsequently, we categorize cryptography techniques for analyzing contemporary lightweight, authenticated, and key agreement protocols. Moreover, we present a comparative analysis of the performance, efficiency, and security features in cryptographic solutions. Finally, we identify challenges related to smart grid, cybersecurity, and cryptographic techniques, along with outlined recommended future research directions. The significance of this study lies in providing valuable insights into the performance and security of cryptographic techniques. It supports researchers who may consider tested cryptographic solutions to advance data privacy and cybersecurity within SG’s multifunctional layers of infrastructure.

The use of SCADA and AMI systems in smart grid-based Industrial Internet-of-Things (SG-IIoT) networks for proper energy supply are noteworthy. Inaccurate energy load forecasts, cyber-threats, and energy load-based sustainability issues in smart grids hinder SG-IIoT operations. To mitigate these challenges, a federated-learning approach is developed by integrating LSTM (Long-Short-Term-Memory), Transformer-LLM (Larger Language Model) based Foundation-Model, and AES (Advanced-Encryption-Standard) cryptography. The proposed approach is named Responsible-Federated-Foundation-Model (ResFedFM). To ensure secure federated learning computation as well as data security at the edge (smart meter), fog (SCADA-based substation grid) and cloud (grid cloud server) layers of the SG-IIoT, a self-parent keys-based cryptography method has been developed by combining AES with HMAC (Hash-based-Message-Authentication-Code). A load forecasting algorithm called LSTM-LLM-GenResAI-Forecasting has been developed for computation at each end node of the federated learning process. The edge node forecast outputs are encrypted and aggregated at the fog node. At the fog node, the data are decrypted, and aggregation algorithm of federated-learning process are used to generate overall load forecasting of each sub-station grid. Again, the forecast data from these fog nodes are aggregated in an encrypted state at the cloud level and overall load forecasts are generated for multiple fog nodes. The result of proposed approach provides responsible forecasting (High accuracy, green computing-based energy demand, optimization of AI-hallucination, and grid data security), demonstrating enhanced performance over seven significant models.

Mobile Edge Computing (MEC) is capable of inheriting cloud and Internet of Things (IoT) resources to the brim of the computational and communication networks. Conservatively, the MEC is outsourced from the IoT/ cloud platforms to Maximize the workflow and task completion abilities. However, due to outsourcing features, the security requirements for workflow scheduling and task completion rely on trusted device selection and high normalization. To satisfy these security demands, this article introduces a secure workflow scheduling algorithm using the knowledge learning concept. The proposed algorithm verifies the operative and failing device features under diverse allocation parameters. Based on the workflow completion lag, new scheduling or offloading decisions are Made. The decision support is provided by the knowledge learning is retains the previous operational status of the edge devices through stage-based updates. The stages for scheduling, classification, and offloading are updated periodically to Maximize the device selection and to reduce overhead in the process. Thus the consolidated process is adaptable to scheduling, offloading, and workflow completion regardless of the devices, allocation time, and device selection processes. This proposed algorithm is reliable in improving the normalized security by 14.17% by reducing the device selection overhead by 12.82% for the maximum allocation rates.

The price gouging or price hike is a worldwide issue, and it is related to inflation. Because of rising prices, people in various countries cannot afford nutritious food or proper treatment. Sometimes shops, restaurants, and transportation service providers charge more than the prescribed product price from buyers. In addition, unauthorized VAT or Tax is taken on products that the government exempts. Another reason for price hikes is bribery, and it occurs in transporting and delivering goods. This article introduces a blockchain-based Internet of Things model to monitor product price hikes and corruption from the Industry 4.0 and blockchain 5.0 point of view. Industries produce and package different products. Wholesalers and retailers purchase products from industrial companies. The primary goal of this article is to propose a blockchain mechanism for monitoring price hikes and corruption where the government can monitor buying and selling between buyers and industrial companies. Here, we have established blockchain-integrated remote database model where blockchain relates to a relational database management system that uses remote database access protocol and Cloud server. This article presents the brief evolution of blockchain and industry generations. Finally, this article provides a next generation blockchain model. An intelligent government connected with Industry 4.0 monitors price hikes and corruption.

The Internet of Medical Things (IoMT) mobile devices such as ambulance, medical done, and emergency mobile medical equipment face severe signal distortions due to interference, end-to-end packet loss, handoff delays, and lower throughputs during mobility. Network mobility basic support protocol (NBSP) has been proposed using the IP-based Wi-Fi solution to solve these issues. However, the weak signal, extra signaling overhead, and higherdelays were identified during handover due to patients' excessive requisites, resulting in radio link failure. Therefore, this article proposes a novel resource-efficient flow-enabled distributed mobility anchoring (FDMA) framework enhancing the functionalities of the centralized network entities and mobility entities.The performance of the proposed FDMA framework is evaluated and compared with the standard NBSP and proxy NEMO (PNEMO) scheme in terms of the variable number of cell residence time and mobile routers, where the proposed framework outperformed NBSP and PNEMO schemes for IoMT Mobile devices in 5G network.

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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.