The ability to move and hover has made rotary-wing Unmanned Aerial Vehicles (UAVs) suitable platforms to act as Flying Communications Relays (FCR), aiming at providing on-demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on-board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy-efficiency from the communications point of view, overlooking the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion.
In this article we propose Energy-aware RElay Positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy-efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the Quality of Service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation.
TRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs
Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）；School of Automation Science and Electrical Engineering at Beihang University（北京航空航天大学自动化科学与电气工程学院）；State Key Laboratory of Intelligent Manufacturing Systems Technology（智能制造系统技术国家重点实验室）；School of Transportation, Southeast University（东南大学交通学院）；Digital Health Research Center, Haihe Lab of ITAI（海河信息技术与人工智能实验室数字健康研究中心）；School of Cyber Science, Nankai University（南开大学网络空间科学学院）；China-SCO Digital Intelligence Applications (DIA) Joint Laboratory（中国-上海合作组织数字智能应用联合实验室）；College of Engineering, Abu Dhabi University（阿布扎比大学工程学院）；Faculty of Engineering and Architecture, Nisantasi University（尼桑塔西大学工程与建筑学院）；School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）
UAV-enabled Computing Power Networks: Design and Performance Analysis under Energy Constraints
Lingnan University（岭南大学）；Hong Kong JC STEM Lab of Smart City（香港赛马会智慧城市STEM实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机系）；Department of Biomedical Engineering, City University of Hong Kong（香港城市大学生物医学工程系）
