Trajectory Design And Resource Allocation Of Uav For Data Collection In WSNs

Wireless sensor network(WSN),which integrates induction,data processing and communication,is one of the key technologies of Ubiquitous Network in 6G era.For the uplink transmission,the power-limited sensors limit the transmission performance of the WSNs.Researchers have introduced unmanned aerial vehicles(UAVs)to WSNs to establish better communication channels of the sensors.With the aid of the flexible mobility and high altitude advantage of the UAV,energy consumption of the sensors is reduced when maintaining the uplink transmission rate.However,UAVs are constrained by size,weight and power(SWAP),and thus have limited abilities.Fortunately,if we deploy the UAV and the ground base station(GBS)to cooperatively collect data in the WSNs,we can combine their advantages to prolong the lifetime and reduce the cost of maintenance and redeployment of the WSNs.However,few research has been conducted on the co-deployment of UAV and ground base station(GBS)for data collection with the aim of minimizing the energy consumption of the sensors.In this paper,we focus our attention on the data collection in a WSN.Specifically,UAV and GBS are introduced to cooperatively complete the data collection tasks in WSNs.By planning the trajectory of the UAV and distributing the system communication resources,we reduce the energy consumption of the sensors.This work mainly study the UAV trajectory design and resource allocation for data collection in two scenarios.For fixed-sensors scenario,we analyze the communication resources distribution between the UAV and the GBS,and optimize the trajectory of the UAV to minimize the total energy consumption of all sensors.Moreover,we consider a randomly deployed sensors scenario,which suffers high computational and time complexity due to massive sensors with unknown locations.Based on optimizing the resource distribution between the UAV and the GBS,we propose a UAV trajectory optimization algorithm with pre-determined mobility patterns to facilitate the trajectory planning.In this way,we reduce the problem complexity and simultaneously minimize the total energy consumption for uplink transmission of all the sensors.Numerical results show that the proposed design reduces the energy consumption of all sensor nodes.