Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management

被引:11
|
作者
Sami, Hani [1 ]
Saado, Reem [2 ]
El Saoudi, Ahmad [2 ]
Mourad, Azzam [2 ,3 ]
Otrok, Hadi [4 ]
Bentahar, Jamal [1 ,4 ]
机构
[1] Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ H3G 1M8, Canada
[2] Lebanese Amer Univ, Dept CSM, Cyber Secur Syst & Appl AI Res Ctr, Beirut 1104, Lebanon
[3] New York Univ, Div Sci, Abu Dhabi, U Arab Emirates
[4] Khalifa Univ, Dept EECS, Ctr Cyber Phys Syst, Abu Dhabi, U Arab Emirates
来源
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT | 2023年 / 20卷 / 03期
关键词
UAV; OBU; IoV; container placement; memetic algorithm; localization; machine learning; UNMANNED AERIAL VEHICLES; INTERNET;
D O I
10.1109/TNSM.2023.3242205
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from Internet of Things (IoT) devices, such as radars, sensors, and actuators. Consequently, overwhelming the existing infrastructure, namely cloud, and Road Side Units (RSU), reduces the quality of service (QoS) experienced by vehicular users. Accordingly, this paper contributes in proposing a new architecture for using Unmanned Ariel Vehicles (UAVs) and On-Boarding Units (OBUs) working in collaboration to achieve a significantly improved QoS. The proposed framework offers an end-to-end solution for master election, cluster management and recovery, vehicle selection, service placement, and accurate localization of vehicles. A QoS improvement is possible through an efficient cluster formation and placement solution that assigns lightweight services, as containers, to OBUs and UAVs while meeting various objectives. The efficiency of the proposed scheme originates from the use of the evolutionary Memetic Algorithm that 1) respects the mobility and energy constraints of UAVs and OBUs, 2) meets the user demands, and 3) uses machine learning for the accurate localization of vehicles. Our experiments using the Mininet-WiFi and SUMO simulators show at least 30% improvement in terms of QoS compared to a state-of-the-art solution.
引用
收藏
页码:3428 / 3442
页数:15
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