Data Collection Optimization In UAV-enabled IoT Networks

The contradiction between insufficient energy supply and energy consumption of Internet of Things(IoT)devices becomes a bottleneck that restricts the fast development of IoT networks.Due to the mobility and flexibility of unmanned aerial vehicles(UAVs),they can be applied in IoT networks to perform single-hop data collection from IoT nodes,and thus greatly reduce the energy consumption comparing with the traditional multi-hop data collection in IoT systems.To solve the insufficient energy supply problem in IoT devices,by employing the wireless power transfer technology,UAVs can transmit energy to IoT devices and extend their life time.To reduce the devices’ energy consumption,backscatter communication technology can be used to reduce the energy consumption of information transmission for IoT devices through passive transmission.By combining the advanced wireless power transfer and backscatter communication technologies,this thesis aims at investigating the data collection performance optimization problems in UAV based on IoT systems.This research can alleviate the contradiction between the energy shortage and energy consumption of IoT devices,significantly improve the data collection performance,and finally satisfy the diverse needs of data collection in IoT networksFirstly,the data collection problem in an IoT network with multiple UAVs is investigated.In the studied problem,the UAVs first power multiple IoT devices by wireless power transfer,and then IoT devices utilize the harvested energy to transmit data to UAVs.Different from most existing works that often assume that the channel between the UAV and the IoT device is a simplified line-of-sight(LoS)channel,a more practical and accurate probabilistic LoS channel model is adopted.In this channel model,both the elevation angle and the distance between the UAV and the IoT device determine the channel gain.The objective is to maximize the UVA’s minimum data collection rate among all IoT devices by jointly optimizing the energy transmission time,the information transmission time,and the three-dimensional(3D)trajectory of UAVs within a limited time duration.This results in a non-convex optimization problem,which is challenge to solve.To tackle this difficulty,the non-convex problem is transformed to a difference of convex(D.C.)optimization problem by subtly using several methods.To solve the D.C.optimization problem,an efficient iterative algorithm is designed via the successive convex approximation method.Numerical simulation results are provided to show the performance of the proposed algorithm under various conditions,and verify that the proposed algorithm is superior to two benchmark algorithms,the algorithm with the simplified LoS model and that with 2D trajectory optimization.Secondly,to satisfy the requirement of age of information(AoI)in IoT networks,the average AoI minimization problem for all IoT devices is studied when a UAV collects data from IoT devices.To reduce IoT devices’energy consumption,the UAV utilize backscatter communication to collect data from IoT devices.And to achieve ultra-reliable low-latency data transmission,IoT devices adopt finite blocklength(FBL)transmission to transmit data to the UAV.The goal is to minimize the average AoI of all IoT devices by jointly optimizing the UAV’s data collection time for different devices,UAV’s transmission power,and the data collection sequence of IoT devices.Since the problem contains both discrete variables(the data collection sequence of IoT devices)and the continuous variables(transmission time and transmission power),it is generally very difficult to solve by conventional methods.Through analyzing the problem’s structure characteristics,one finds that it can be divided into two sub-problems without affecting the optimal solution.The two sub-problems are the data collection time minimization sub-problem and the IoT devices’ data collection sequence sub-problem.To solve the first sub-problem,an iterative algorithm based on the successive convex approximation is proposed.And to solve the second sub-problem,a backtracking and pruning algorithm and a genetic algorithm are designed to obtain the optimal and suboptimal solution,respectively.Numerical simulation results show that the proposed two algorithms can always achieve much better performance in terms of the average AoI than the benchmark greedy algorithm.