Computing on Wheels: A Deep Reinforcement Learning-Based Approach

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 11; pp. 22535 - 22548
• Main Authors: Kazmi, S. M. Ahsan, Ho, Tai Manh, Nguyen, Tuong Tri, Fahim, Muhammad, Khan, Adil, Piran, Md. Jalil, Baye, Gaspard
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Computation offloading; Costs; Deep learning; deep reinforcement learning; Dynamic scheduling; Edge computing; Electric vehicles; Energy consumption; Network latency; Next-generation intelligent transport system; Roadsides; Task analysis; task offloading; Vehicle dynamics; vehicle-to-vehicle communication; Vehicular ad hoc networks

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporatin...