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

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 experi...

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Bibliographic Details
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
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Summary: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.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/4173346