Data Harvesting and Path Planning in UAV-aided Internet-of-Things Wireless Networks with Reinforcement Learning : KTH Thesis Report

Abstract: In recent years, Unmanned aerial vehicles (UAVs) have developed rapidly due to advances in aerospace technology, and wireless communication systems. As a result of their versatility, cost-effectiveness, and flexibility of deployment, UAVs have been developed to accomplish a variety of large and complex tasks without terrain restrictions, such as battlefield operations, search and rescue under disaster conditions, monitoring, etc. Data collection and offloading missions in The internet of thingss (IoTs) networks can be accomplished with the use of UAVs as network edge nodes. The fundamental challenge in such scenarios is to develop a UAV movement policy that enhances the quality of mission completion and avoids collisions. Real-time learning based on neural networks has been proven to be an effective method for solving decision-making problems in a dynamic, unknown environment. In this thesis, we assume a real-life scenario in which a UAV collects data from Ground base stations (GBSs) without knowing the information of the environment. A UAV is responsible for the MOO including collecting data, avoiding obstacles, path planning, and conserving energy. Two Deep reinforcement learnings (DRLs) approaches were implemented in this thesis and compared.