UAV-assisted Connectivity Enhancement Algorithms In Multiple Wireless Sensor Networks

In recent years,wireless sensor networks(WSNs)have been widely used in the Internet of Things(IoT)to expand network functions.However,the connectivity of WSNs in IoT will encounter new challenges when the network is divided into multiple isolated sub-sensor networks(MISN)by obstacles.Mobile nodes have been used as relays to connect partitioned networks.However,mobile nodes have the problem of not being able to move or moving slowly when encountering obstacles.To solve the connectivity enhancement problem of MISN,the Unmanned Aerial Vehicle(UAV)is used as a communication relay node,and a UAV-assisted connectivity enhancement scheme is proposed.The research contents of this thesis are summarized as follows:(1)Aiming at the MISN with weak real-time connectivity requirements,a new definition of connectivity in MISN is proposed,and the UAV assisted connectivity enhancement algorithms(UCE)are designed.Our target is to minimize energy consumption of the UAV while satisfying MISN connectivity requirements.Firstly,the destination selection ant colony optimization algorithm(DSACO)and the normalized ant colony optimization algorithm(NACO)are proposed to connect all the sub-sensor networks.Through comparative analysis of them,the optimal path for the UAV to solve the above optimization problem is found.Secondly,autonomously generated optimal point ant colony optimization algorithm(AGOP)is proposed to connect non-communicable nodes within each sub-sensor network.Simulation results show that the complexity of the three algorithms is low,and they can complete the connectivity enhancement task of a large outdoor MISN with reduced energy consumption of the UAV,and the connectivity of the MISN has been significantly improved.(2)Aiming at the MISN with strong real-time connectivity requirements,three types of UAV swarm modes are proposed,and UAV swarm assisted connectivity enhancement algorithms(Us CE)are designed.A UAV swarm with high degree of freedom and flexibility provides a new way in IoT for solving the above connectivity problem.Our target is to find an optimal solution that minimizes the number of UAVs in the swarm and maximizes the connection time of MISN.We divide the working modes of a UAV swarm into hovering and flying.Firstly,the ground sink nodes are classified by a MISN’s sink node classification algorithm to generate hovering points for the UAV swarm.Secondly,the results are optimized and adjusted by a minimum UAV swarm hovering connection algorithm to obtain an optimal solution under the hovering mode.Finally,we achieve an optimal connectivity when the UAV swarm works in flying mode through the UAV swarm flight connectivity algorithm and compare it with two previously proposed algorithms.Simulation results show that the complexity of the algorithms is low,the connection time of MISN increases significantly,and the number of UAVs is small.