Energy Efficiency Optimization Of UAV-Assisted Dynamic IoT Data Collection System

With the rapid development of the Internet of Things(IoT),data collection has become a huge problem.UAVs,which are mobile,flexible,rapidly deployable and low-costs,offer new opportunities.However,limited battery capacity introduces challenges to UAV communication systems.In addition,considering that the working state of device nodes is closely related to the type of service they support,the dynamic working state over time makes it more challenging to collect data efficiently.Based on this,this thesis studies the energy efficiency of UAV-assisted dynamic IoT for data collection,and puts forward an optimization schemes to meet the realistic communication requirements and to improve the performance of systems.The main contents and innovations of this thesis are as follows:A maximizing energy efficiency of UAV data collection system based on free-space is proposed to solve the energy issue.To improve the energy efficiency of UAV when the real-time working status of device nodes is unknown to UAV,a joint optimization algorithm is proposed to alternately optimize UAV scheduling scheme,UAV’s 3D trajectory and transmit power.By analyzing the system performance and comparing with other benchmark solutions,the effectiveness of the proposed joint optimization scheme is proved.A maximizing energy efficiency of multi-UAV data collection system.To further improve the performance in actual application scenarios,Multiple fixed-wing UAVs are deployed in different regions to collect data in flight mode.To maximize energy efficiency,an optimization algorithm based on successive convex approximation algorithm and Dinkelbach algorithm is proposed to jointly optimize the scheduling scheme,trajectory and velocity of UAVs.Finally,the algorithm complexity and convergence are analyzed.The simulation results show that the proposed scheme can significantly improve the system energy efficiency compared with other schemes.