UAV Placement Optimization for Internet of Medical Things

Internet of Medical Things (IoMT), intended for real-time health monitoring, are generating quantity of health data such as electrocardiogram, oxygen saturation, and blood pressure every second. The captured data should be processed and analyzed in a delay sensitive way which is vital to the survival rate for cardiovascular and cerebrovascular diseases. In this regard, Unmanned Aerial Vehicles (UAVs) have already demonstrated the enormous potentials. To begin with, due to better line-of-sight, wider communication and more flexible on-demand deployment, UAVs can realize seamless wireless connection to IoMT. Furthermore, UAVs can act as fog nodes to provision services for IoMTs such as task performing and data analysis. We in this paper focus on a sub-problem, i.e., the placement of UAVs over the serving area when they function as fog nodes. In the airborne fog computing, the placement of UAVs has an important influence on energy consumption and exploration area, let alone the communication coverage of the personal health devices on the ground. Therefore, we in this paper propose a particle swarm optimization (PSO) based algorithm to optimize the UAV placement over the serving area for the IoMT devices. We have conducted extensive simulations to evaluate it. The results show that our approach can significantly reduce the number of UAVs needed to deploy while considering the communication coverage and other factors.This work was partially supported by the project “P-CL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications (LZC0019)”, the National Funding from the FCT - Fundac¸ão para a Ciência e a Tecnologia through the UID/EEA/50008/2019 Project, and by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5, Chinese National Research Fund (NSFC) Key Project No. 61532013 and No. 61872239; 0007/2018/A1, 0060/2019/A1, DCT-MoST Joint-project No. 025/2015/AMJ of Science and Technology Development Fund, Macao S.A.R (FDCT), China, and University of Macau Grant Nos: MYRG2018-00237-RTO, CPG2019-00004-FST and SRG2018-00111-FST. Chunsheng Zhu is the corresponding author