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.

In the era of smart cities, securing sensitive data transmitted through Internet of Things (IoT) devices has become a critical challenge. This paper presents a novel Fuzzy-Tuned Intelligent Edge-based Image Steganography framework designed to enhance IoT communication security. The proposed method employs a weighted fuzzy logic system to detect meaningful edge regions in a cover image, guided by gradient magnitude and local entropy to assign edge strength, enabling the selection of fine, imperceptible edge pixels in a 4-MSB image. To ensure optimal embedding locations, a Particle Swarm Optimization (PSO) algorithm is applied to intelligently select high-entropy edge pixels, maximizing both imperceptibility and embedding capacity. The secret data is then embedded within these optimized edge regions, preserving visual fidelity while ensuring robust concealment. Comprehensive testing was carried out on widely used benchmark datasets, with evaluation based on indicators like visual analysis of cover-stego image and their corresponding histogram plots. In addition, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and entropy are evaluated for further analysis. The findings reveal that the introduced approach provides enhanced data protection, robustness, and image quality when compared with conventional edge-detection and random embedding techniques. This model proves highly effective for secure information exchange in low-resource IoT systems integrated into smart city networks.

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.

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.

Abstract The Internet of Vehicles (IoV) and Vehicle‐to‐Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud‐radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high‐speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi‐layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

Abstract The recent advancements of Internet of Things (IoT) embedded systems, wireless networks, and biosensors those have assisted in the rapid development of implanting wearable sensors are reviewed here. The applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services are also reviewed here. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain‐based approaches that can help in improving the confidentiality of IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Despite multiple security threats, there are novel cryptographic techniques, such as access control, identity authentication, and data encryption that can help in improving the security and reliability of IoMT devices.

Abstract Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long‐term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short‐term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell‐Radio Networks Temporary Identifiers (TMSIs and C‐RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C‐RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.

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.

The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.