Energy-Efficient and Secure Framework for Computation Offloading in Sustainable Vehicular Edge-Cloud Networks
Ibrahim A. Elgendy · 2024
Recently, Internet of Vehicles (IoV) and their applications have gained considerable growth and attention in our daily life, including autonomous vehicles, intelligent traffic management, fleet management and smart cities integration. However, this category of applications cannot be handled by these devices due to their limited computation capabilities. Vehicular Edge-Cloud Computing (VECC) paradigm offers a promising solution for remote task execution by leverages both edge and cloud infrastructure. Nevertheless, ensuring data security and optimizing energy consumption within this paradigm remains a critical challenge. To this end, in this paper, in addition to introducing a novel security layer to mitigate security vulnerabilities, we propose a new intelligent caching strategy for VECC networks. Specifically, to protect data during transmission, a new cryptographic technique based on Advanced Encryption Standard (AES) is proposed that utilizes Electrocardiogram (ECG) signals as keys. Additionally, a new caching mechanism is designed to cache the completed tasks at the RSU, and thereby reducing latency and energy. Moreover, an optimization model is formulated to minimize energy consumption while meeting stringent latency requirements during computation offloading. The simulation results ultimately demonstrate that our model is capable of achieving a substantial reduction in energy consumption compared to existing benchmark approaches.