Study On The Data Collection Method Of Internet Of Things Supported By UAV Cluster Collaboration

Using traditional multi-hop communication methods to collect data perceived by IoT devices in the Internet of Things(IoT)system will not only reduce the network lifetime,but also seriously affect the timeliness of the data.Deploying Unmanned Aerial Vehicle(UAV)carrying micro base stations to assist in data collection can effectively make up for the lack of communication resources on the ground,but UAV battery capacity is limited,once a large data transmission situation is encountered,a single UAV cannot meet the system's quality of service(QoS)requirements.Using multiple UAVs to form a cluster to collect data in parallel in a large-scale IoT can effectively improve the data transmission efficiency of the network.Considering the high purchase cost of UAV,under the condition of meeting the QoS requirements of IoT devices,the number of UAVs used should be minimized.This article will proceed from the above considerations and design an efficient IoT data collection method supported by UAV cluster collaboration.For the parallel data collection of the UAV cluster,this paper chooses UAV to collect data only when hovering.Firstly,in order to improve the collection efficiency,this paper proposes a multi-UAVs coverage area division and optimal hovering position selection method,constructs a parallel data collection network supported by multiple UAVs,and gives a detailed network system model.Then mathematically derive the throughput expression of each IoT device.Secondly,studying how to cluster the IoT devices in the network coverage area and obtain the optimal hovering position of UAVs in each cluster,to maximize the average throughput of IoT devices while balancing the number of IoT devices in each cluster.Finally,a heuristic algorithm is used to solve the optimization model,and the effectiveness of the algorithm is verified by simulation.According to the obtained IoT device clustering and the UAV hovering position in each cluster,this paper proposes a mechanism for minimizing the number of UAVs,considering the data collection delay and energy consumption constraints of the UAV,constructing a mathematical model of the problem.The proposed formulation problem is a mixed integer non-convex optimization problem,so finding its optimal solution is a non-deterministic polynomial hard(NP-Hard)problem.Therefore,this paper designs a low-complexity iterative algorithm to find the sub-optimal solution of the problem,that is,decomposing the original problem into two sub-problems and solving iteratively.For the first sub-problem,i.e.,multi-UAVs flight path planning,assuming that the number of UAV is known,in order to improve the efficiency of UAV cluster and ensure the fairness of each UAV assignment,this paper presents a time-balanced multi-UAVs flight path planning method,optimizing the flight trajectory of each UAV to minimize the total mission completion time,while balancing the operating time of each UAV.For the second sub-problem,that is,based on the known UAV flight trajectory,and on the basis of satisfying the data collection QoS,the original problem is further simplified to a problem of minimizing the number of UAVs,and an exhaustive search method is used to find the minimal number of UAVs.Finally,this paper integrates the previous multiUAVs coverage region partition and hovering position selection algorithm,multi-UAV flight path planning algorithm,and the exhaustive search algorithm to determine the number of UAVs,iteratively obtains the sub-optimal solution of the original NP-Hard problem,and verifies the effectiveness of the proposed iterative algorithm through simulation results.