Energy-Efficient and Load-Balanced Edge-Driven Vehicular Network Using Intelligent Task Offloading

Intelligent Transportation System (ITS) interconnects smart technologies for the advancement in communication and autonomous decision making in vehicle interactions. It manages traffic control infrastructure, analyses road conditions, and supports cooperative awareness in a crucial environment. The sensors continuously collect real-time vehicle data, process it, and forward it to analysis servers to predict the behavior of Vehicular Ad hoc Networks (VANETs). Many approaches have been proposed to address research challenges in routing and improve communication for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. However, due to dynamic topology, the network becomes disturbed and loses established connections, leading to instability in data transmission against unpredictable behavior of the network. This research presents a framework, referred to as Energy Efficient Load Balanced Edge-Driven Vehicular Networks (EELB-EVN), that aims to attain load-balanced communication across vehicles and the interconnected infrastructure, thereby preventing congestion and reducing computational cost. In addition, an Intelligent offloading technique is developed using the Analytical Hierarchy Process (AHP) to reduce the additional overhead on the devices, thus enabling energy-aware and latency-sensitive vehicular communication. Furthermore, trustworthiness strategies are explored to enhance reliability and ensure the credibility of information. The simulation tests revealed the significance of the proposed framework compared to related schemes in terms of energy consumption and latency by 20% to 25%, task success rate and network throughput by 30% to 40%, and computational complexity by 33% across dynamic vehicular scenarios.