2018/07/05 by Sanaa Sharafeddine, Sharafeddine, Sanaa, Rania Islambouli +1
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Robotic Path Planning Algorithms #UAV Applications and Optimization
paper · pdf · doi:10.48550/arxiv.1807.02009
openalex publication_date 2018/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of\napplications in wireless networks leading to attractive business opportunities.\nIn the case of abrupt disruption to existing cellular network operation or\ninfrastructure, e.g., due to an unexpected surge in user demand or a natural\ndisaster, UAVs can be deployed to provide instant recovery via temporary\nwireless coverage in designated areas. A major challenge is to determine\nefficiently how many UAVs are needed and where to position them in a relatively\nlarge 3D search space. To this end, we formulate the problem of 3D deployment\nof a fleet of UAVs as a mixed integer linear program, and present a greedy\napproach that mimics the optimal behavior assuming a grid composed of a finite\nset of possible UAV locations. In addition, we propose and evaluate a novel low\ncomplexity algorithm for multiple UAV deployment in a continuous 3D space,\nbased on an unsupervised learning technique that relies on the notion of\nelectrostatics with repulsion and attraction forces. We present performance\nresults for the proposed algorithm as a function of various system parameters\nand demonstrate its effectiveness compared to the close-to-optimal greedy\napproach and its superiority compared to recent related work from the\nliterature.\n