AI-Enabled Energy-Efficient Fog Computing for Internet of Vehicles

• Published in: Journal of sensors 2022-05, Vol.2022, p.1-14
• Main Authors: Tariq, Hira, Javed, Muhammad Awais, Alvi, Ahmad Naseem, Hasanat, Mozaherul Hoque Abul, Khan, Muhammad Badruddin, Saudagar, Abdul Khader Jilani, Alkhathami, Mohammed
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud computing; Communication; Computation offloading; Electric vehicles; Electronic devices; Energy consumption; Energy efficiency; Greedy algorithms; Internet; Internet of Things; Internet of Vehicles; Iterative algorithms; Iterative methods; Markov analysis; Markov processes; Optimization; Power; Smart sensors; Traffic congestion; Traffic safety
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2022/4173346

Future autonomous electric vehicles (EVs) are equipped with several IoT sensors, smart devices, and wireless adapters, thus forming an Internet of Vehicles (IoVs). These intelligent EVs are envisioned to be a promising solution for improving transportation efficiency, road safety, and driving experience. Vehicular fog computing (VFC) is an evolving technology that allows vehicular application-related tasks to be offloaded to nearby computing nodes and process them quickly. A major challenge in the VFC system is to design energy-efficient task offloading algorithms. In this paper, we propose an optimal energy-efficient algorithm for task offloading in a VFC system that maximizes the expected reward function which is derived using the total energy and time delay of the system for the computation of the task. We use parallel computing and formulate the optimization problem as semi-Markov decision process (SMDP). Bellman optimal equation is used in value iteration algorithm (VIA) to get an optimal scheme by selecting the best action for the current state that maximizes the energy-based reward function. Numerical results show that the proposed scheme outperforms the greedy algorithm in terms of energy consumption.