Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks

• Published in: IEEE internet of things journal Vol. 9; no. 21; pp. 21548 - 21560
• Main Authors: Sabzehali, Javad, Shah, Vijay K., Fan, Qiang, Choudhury, Biplav, Liu, Lingjia, Reed, Jeffrey H.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximate algorithms; Atmospheric modeling; Autonomous aerial vehicles; backhaul; Base stations; Cellular communication; Connectivity; graph theory; Greedy algorithms; Integer programming; Internet of Things; Linear programming; multi-UAV networks; Networks; Optimization; Placement; Radio equipment; Rural areas; Three-dimensional displays; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3184323

Multi unmanned aerial vehicle (UAV) network is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3-D placement of all UAV base stations (BSs) such that the formed multi-UAV network: 1) utilizes a minimum number of UAVs while ensuring-2) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial BS; and 3) wireless coverage to all ground users in the area of operation. This joint backhaul-and-coverage-aware drone deployment (BoaRD) problem is largely unaddressed in the literature and, thus, is the focus of this article. We first formulate the BoaRD problem as integer linear programming (ILP). However, the problem is NP-hard and, therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches-Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.