UAV trajectory planning algorithmfor data collection in wireless sensor networks

In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.

3D non-stationary geometry-based stochastic model for unmanned aerial vehicle air-to-ground multi-input multi-output channels

A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed.The scatterers surrounding the UAV and ground station are assumed to be distributed on the surface of two cylinders in the proposed model.The impact of UAV rotations and accelerated motion is considered to describe channel non-stationarity.The computational methods of the corresponding time-variant parameters,such as UAV antenna array angles,time delays,and maximum Doppler frequencies,are theoretically deduced.The model is then used to derive channel statistical properties such as space-time correlation functions and Doppler power spectral density.Finally,numerical simulations are run to validate the channel s statistical properties.The simulation results show that increasing the UAV and ground station accelerations can reduce the time correlation function and increase channel non-stationarity in the time domain.Furthermore,the UAV s rotation significantly influences the spatial correlation function,with rolling having a greater influence than pitching.Similarly,the different directions of UAV movement significantly impact the Doppler power spectral density.