期刊
IEEE ACCESS
卷 8, 期 -, 页码 967-979出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2961977
关键词
Unmanned aerial vehicle swarms; heterogeneous IoT networks; wireless power transfer; stochastic geometry; energy efficiency
资金
- Natural Science Foundation of Beijing Municipality [19L2022, 4202046, 4204099, KZ201911232046]
- National Natural Science Foundation of China [61801052, 61801434]
- Science and technology Project of Beijing Municipal Education Commission [KM202011232002, KM202011232003]
- Key Research and Cultivation Project at Beijing Information Science and Technology University [5211910926, 5211910924]
- Supplementary and Supportive Project for Teachers at Beijing Information Science and Technology University [5029011103, 5111911147]
- Zhengzhou Municipal Science Technology Innovation Project [2019CXZX0037]
- China Postdoctoral Science Foundation [2018M642784]
- Scientific and Technological Key Project of Henan Province [192102310178]
- Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Industry and Information Technology [KF20181901]
An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.
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