Optimizing Energy Efficiency in Vehicular Edge-Cloud Networks Through Deep Reinforcement Learning-Based Computation Offloading

Vehicular Edge-Cloud Computing (VECC) paradigm has emerged as a viable approach to overcome the inherent resource limitations of vehicles by offloading computationally demanding tasks to remote servers. Despite its potential, existing offloading strategies often result in increased latency and sub-optimal performance due to the concentration of workloads on a limited number of connected Roadside Units (RSUs). Moreover, ensuring data security and optimizing energy usage within this framework remain significant challenges. To address these concerns, this paper proposes a comprehensive framework for VECC systems. A novel load-balancing algorithm is proposed to effectively redistribute vehicles among RSUs, considering factors such as RSUs load, computational capacity, and data rate. Additionally, a robust security mechanism is incorporated using the Advanced Encryption Standard (AES) in conjunction with Electrocardiogram (ECG) signals as encryption keys to enhance data protection during transmission. To further improve system efficiency, a novel caching strategy is introduced, enabling edge servers to store completed tasks, which in turn reduces both latency and energy consumption. An optimization model is also proposed to minimize energy expenditure while ensuring that latency constraints are satisfied during computation offloading. Given the complexity of this problem in large-scale vehicular networks, the study formulates an equivalent reinforcement learning model and employs a deep learning algorithm to derive optimal solutions. Simulation results conclusively demonstrate that the proposed model significantly outperforms existing benchmark techniques in terms of energy savings.