The fast growth of the Internet of Everything (IoE) has resulted in an exponential rise in network data, increasing the demand for distributed computing. Data collection and management with job scheduling using wireless sensor networks are considered essential requirements of the IoE environment; however, security issues over data scheduling on the online platform and energy consumption must be addressed. The Secure Edge Enabled Multi-Task Scheduling (SEE-MTS) model has been suggested to properly allocate jobs across machines while considering the availability of relevant data and copies. The proposed approach leverages edge computing to enhance the efficiency of IoE applications, addressing the growing need to manage the huge data generated by IoE devices. The system ensures user protection through dynamic updates, multi-key search generation, data encryption, and verification of search result accuracy. A MTS mechanism is employed to optimize energy usage, which allocates energy slots for various data processing tasks. Energy requirements are assessed to allocate tasks and manage queues, preventing node overloading and minimizing system disruptions. Additionally, reinforcement learning techniques are applied to reduce the overall task completion time using minimal data. Efficiency and security have been improved due to reduced energy, delay, reaction, and processing times. Results indicate that the SEE-MTS model achieves energy utilization of 4 J, a delay of 2s, a reaction time of 4s, energy efficiency at 89%, and a security level of 96%. With computation time at 6s, SEE-MTS offers improved efficiency and security, reducing energy, delay, reaction, and processing times, although real-world implementation may be limited due to the number of devices and incoming data.

Cyber-physical systems have improved modern transportation by allowing vehicles and road systems to communicate through Vehicular Ad Hoc Networks (VANETs). Existing anomaly detection approaches often struggle with high false-positive rates, poor adaptability, and significant computational demands, compromising their real-time efficacy and scalability. To address these problems, this research presents an Anomaly Detection using Machine Learning Algorithms (AD-MLA) framework that employs a Random Forest model to accurately detect abnormal activities. The framework encompasses feature selection, data clustering, and an energy-efficient routing strategy that incorporates node energy, signal strength, hop count, and link stability. Evaluations demonstrate that AD-MLA reduces false alarms, improves detection accuracy, and operates with lower energy and computational requirements. It offers a smart, rapid, and efficient security system for real-time VANET environments, rendering it appropriate for transportation systems characterised by high reliability and safety. By integrating a Random-Forest-based anomaly detector with intelligent feature selection and an energy-efficient routing method that accounts for residual energy, signal strength, and link stability, the suggested framework systematically addresses these challenges. This approach delivers 95.33% accuracy, 96.09% recall, 94.25% computational efficiency, and 91.45% resource-use efficiency. This effectively addresses the scalability, latency, and energy challenges that previous systems have faced in incorporating blockchain technology and deep learning architectures.

One potential remedy for grid stability and energy efficiency is the integration of electric vehicles (EVs) into the grid via Vehicle-to-Grid (V2G) technology. The challenge has been figuring out how to best charge and discharge EVs based on driver preferences, electricity rates, and changing grid requirements. Three cutting-edge approaches are compared in this research as V2G system optimization solutions: adaptive control using Reinforcement Learning (RL), dynamic pricing strategy using Game Theory, and predictive charging using Artificial Intelligence (AI). Predictive models are used in the AI-powered predictive charging method to forecast grid circumstances and adjust the charging time accordingly. To control supply and demand among EVs, grid operators, and the power market, the Game Theory model uses dynamic pricing. In this paper, a hierarchical collaborative fusion method is proposed, where the reinforcement learning control policy applies price signals generated by an artificial intelligence-based demand forecasting methodology, which constrains the game-theoretic pricing layer. Due to such cooperation, V2G systems can make consistent strategic, financial, and operational decisions. Finally, RL provides an adaptive control system that makes optimal charging and discharging decisions in real time. The article highlights the potential to improve energy management in V2G systems, making them more economical, reducing grid congestion, and optimizing sustainability by combining various approaches. The methodologies’ suitability for real-world applications is demonstrated by evaluation with simulated data.

Log-polar or spatially-variant image representation is an important component of active vision system in tracking process for many robotic applications due to its data compression ability, faster sampling rates and hence, direct to faster image processing speed in machine vision system. In this paper, we try to implement log-polar mapping techniques on Xilinx FPGA (field programmable gate array) board for unwarping the omnidirectional images into panoramic images to present a wide angle of view by preserving fine output image quality in a higher data compression manner. Simulations are also run on MATLAB to find out the optimum mapping criterion. Some significant advantages of this new approach are: lighter processing system, lesser space utilization, cost saving, faster processing speed and faster reset time (boot time) compared to a laptop computer that uses MATLAB for doing the unwarping process.