The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block.

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA’s assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA’s involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively.

With the rising adoption of Tactile Internet-driven Consumer Healthcare IoT, ensuring the security and reliability of sensitive healthcare data has become a paramount concern. Threat detection and mitigation in such dynamic and critical environments pose unique challenges. In this context, the main focus of this research is to propose a novel model named Hybrid Ensemble Learning Enabled Sigmoid-Cosine Integrated Pigeon Inspired Feature Selection Based Intrusion Detection (HSPFSID), specifically designed for threat detection and mitigation for Tactile Internet-driven Consumer Healthcare IoT. The proposed model employs a hybrid ensemble learning approach and introduces the innovative Sigmoid-Cosine integrated pigeon-inspired feature selection (SCIPIFS) algorithm. We conducted extensive experiments on four distinct datasets (IoT bot, NSL-KDD, CIC2017, and KDD99) under standard metric measures to evaluate their effectiveness. The results indicate that our proposed work outperforms existing approaches, emphasizing their significant potential in bolstering cyber security measures for healthcare IoT systems.

The Internet of Things (IoT) depends primarily on low-cost wireless sensors with limited energy capacity to allow pervasive monitoring and intelligent control. Nevertheless, unmanned aerial vehicle (UAV) can be used to connect remote terminals that are outside wireless coverage to IoT networks. This solution provides a means of extending the reach of IoT networks, offering more opportunities for monitoring and control. Despite this benefit, the UAV also suffers from low capacity onboard battery. To overcome these problems, solar energy is integrated with UAV, and wireless-powered communication (WPC) techniques are used for IoT terminals. Also, the non-orthogonal multiple access (NOMA) technique can be employed to address the massive connectivity issue of IoT terminals. By leveraging these advantages, we jointly optimize the three-dimensional UAV trajectory and time allocation for WPC powered IoT devices (IoTDs) underlaying solar-powered UAV. To achieve the target, in this paper, introduces a multiagent federated reinforcement learning (MAFAL) algorithm, which concentrates on maximizing energy efficiency (EE) while minimizing energy consumption, guaranteeing quality of service (QoS), fairness, and trajectory planning. The proposed algorithm aims to optimize the overall performance of the system by learning from the collective experience of multiple agents. Simulation result demonstrated that the proposed method achieves 56.84%, 68.45%, and 73.63% higher EE as compared to MAD2PG, DDPG, and DQN, respectively.

Edge-enabled Industrial Internet of Things (E-IIoT) has gained massive attention as a new type of IIoT for hosting emerging low-latency applications. However, due to device variations and complex communication environments, the edge servers and channel information are usually uncertain which poses significant challenges to the computational capacities in practical E-IIoT networks. To address these challenges, in this paper, we introduce a risk-sensitive task offloading scheme for the practical E-IIoT networks. Firstly, a distributed robust offloading optimization problem is formulated by jointly considering the high latency risks caused by network uncertainty and the average latency of the system. Then, by characterizing the latency risk as the conditional value, the formulated problem is transformed into a distributed robust mean conditional value-at-risk (CVaR) optimization problem. Further, we use a fuzzy set to capture the uncertainty of the first and second-order statistical information of the system to overcome the computational difficulties of the problem, and a distributionally robust optimization (DRO) method is employed to transform the mean-CVaR optimization problem into a computable semi-definite programming (SDP) problem. Finally, an improved DRO-based task offloading algorithm is proposed to obtain the optimal decision under uncertain E-IIoT networks. The simulation results show that the proposed offloading scheme not only improves the reliability in computation but also reduces the high latency risks. Therefore, our proposed model is more suitable for practical E-IIoT networks.

Urban sensing systems enable individuals, smart cities, and communities to bring together and share information with unprecedented speed and accuracy. The rapid development of information technology has enabled contact with digital data through the Internet of Things (IoT), smart cities, and the Internet of Medical Things (IoMT). Sensor-based urban smart cities are a new paradigm. A smart city monitors the physical world in real-time and provides smart services to residents in healthcare, transportation, the environment, and energy. Therefore, there is an increasing requirement for a computationally efficient and secure approach for the sensor-based urban city paradigm. We proposed an efficient lossless data hiding scheme based on histogram transformation using dynamic quadtree Nbit localization for urban sensing networks. The proposed scheme is ideally suited for the security, privacy, and exchange of information in urban sensing networks. The system’s valuation and the comparative study with the existing scheme show that the proposed method is secure, more efficient, and obtains high visual qualities (52.23 dB), SSIM, and embedding capacity ratios.