Energy Consumption Aware Delay Minimization for UAV Enabled Internet of Vehicle

• Published in: 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI) pp. 1 - 6
• Main Authors: Emon, Shariar Hossain, Rahman, MD Lotifur, Omi, Sumiya Siddika, Roy, Palash, Razzaque, Md. Abdur, Shadman Wadith, K. M., Ahmed, Jargis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.12.2023
• Subjects: Autonomous aerial vehicles; Collaboration; Energy consumption; Internet-of-Vehicles; Minimization; NP-hard problem; Reinforcement learning; Road side unit; Service Latency; Task Offloading; Unmanned Aerial Vehicles
• DOI: 10.1109/STI59863.2023.10464440

Because of its adaptable nature and excellent maneuvering capabilities, the Unmanned Aerial Vehicle (UAV) can develop a potential solution for executing the Internet of Vehicles (IoV) applications. However, the limited battery capacity of the UAV devices imposes significant barriers to realtime task execution. Existing works are limited to either considering the executing tasks at the UAVs or Road Side Units (RSUs) without considering the collaborative relationship between them for minimizing service delay. In this work, we have presented an optimization framework for task execution of the Internet of Vehicles by exploiting Multi-Uav,Task Executing of Internet of Vehicle by exploiting Multi-UAV namely TEIMU, which minimizes the total average latency by considering energy consumption. The developed TEIMU framework is a combinatorial optimization problem that offloads the tasks by collaboration of RSUs and UAVs. According to the experimental findings, the developed system, TEIMU, surpasses state-of-the-art works in terms of overall average latency and energy usage by up to 10% and 25 %, respectively.