Energy Consumption Aware Delay Minimization for UAV Enabled Internet of Vehicle

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.

I. Introduction

In recent years, the convergence of unmanned aerial vehicles (UAVs) and the Internet of Vehicles (IoV) has opened up new avenues for revolutionizing the landscape of wireless communication networks [1]. The amalgamation of these technologies presents a promising paradigm that holds the potential to address the escalating demands of latency-sensitive vehicular applications such as autonomous driving, realtime traffic analysis, obstacle detection, remote monitoring, etc. [2]. To minimize the service latency, these tasks can be offloaded to the nearby roadside units (RSUs). However, these pose significant challenges in providing realtime services during peak hours. However, due to the high mobility and flexibility characteristics and embedded with multiple sensors, UAVs can collect and sense large amounts of data efficiently [3], [4].