3D deployment of UAVs in wireless networks for traffic offloading and edge computing. (c2019)

Abstract

Unmanned aerial vehicles (UAVs) have recently emerged as enablers for mul- titude use cases in 5G networks leading to interesting industrial and business applications. 5G networks envision a multi-service network promoting various applications with a distinct set of performance and service demands. In this the- sis, we leverage the high exibility, low-cost, and mobility of UAVs to scale up and improve the e ciency of IoT and mobile networks. We study the utilization of UAVs to increase the capacity and coverage in wireless networks on one side and to extend low computational capabilities and mitigate battery limitations in constrained devices on another side. However, to unlock these promising use cases of UAVs, we address the challenges coupled with UAV utilization mainly 3D deployment and device association. First, we address the problem of deploying multiple UAVs to act as aerial base stations (ABS) in 3D space while autonomously adapting their positions as users move around within the network. We formulate the problem as a mixed integer program and then propose a novel autonomous positioning approach that can e ciently gear the UAV positions in a way to maintain target quality re- quirements. Next, we leverage the mobility and agility of UAVs and use them as mo- bile edge servers or cloudlets to o er computation o oading opportunities to IoT devices. This being said, computation tasks generated by IoT devices can be pro- cessed in less latency and with much lower energy consumption at the devices. To optimally deploy UAVs as mounted cloudlets, we formulate our problem as mixed integer program and then use an e cient meta-heuristic algorithm to generate optimized results for large scale IoT networks. The simulation results presented in this thesis demonstrate the e ectiveness of the proposed solutions and algo- rithms compared to the optimal solutions and related work in the literature for various network scenario