Research On Routing And Coverage Optimization Of WSNs Based On Swarm Intelligence Algorithm

As a system of information collection,wireless sensor networks(WSNs)are widely used in various fields for their high flexibility,reliability,and self-organization.It has great value in military,environmental and industrial applications.Wireless sensor networks often operate in relatively harsh environments that are not easily accessible to humans and where the energy of the sensor nodes is difficult to replenish.Therefore,it is of great importance to study how to use the limited energy efficiently and ensure the quality of service of the WSNs.In the network where the sensor nodes are static,it is important for researchers to study how to alleviate the problem of uneven energy consumption and energy holes in the networks through an efficient routing strategy;In the networks where mobile nodes can be deployed,it is crucial to address the problem of low coverage rate and long moving distance of nodes,which determines whether the network can fully utilize the limited energy to provide high-quality network monitoring services.In order to solve these problems,this paper has carried out the following work:1.To solve the problem of uneven energy consumption of nodes and hot areas around base station in the wireless sensor network,a non-uniform clustering adaptive routing algorithm based on improved ant colony algorithm was proposed.The protocol firstly selects candidate cluster heads according to the relative distance and the density of nodes and then the formal cluster heads are selected within the competition radius of candidate cluster heads.Secondly,in the node clustering stage,considering the common edge nodes and the characteristics of non-uniform clustering network,a reasonable clustering structure is formed by the cost function with dynamic weight coefficient.Finally,an improved dynamic transfer strategy and pheromone updating rules combined with local and global pheromone were used in the ant colony routing algorithm,and dynamic pheromone volatilization coefficient was introduced to improve the optimization ability of the routing algorithm and avoid falling into local optimum.The simulation results show that the proposed algorithm can effectively balance network energy consumption,alleviate the hot area problem near the base station,and improve the network life cycle and throughput.Simulation results show that the algorithm can effectively balance the network energy consumption and mitigate the hot zone problem near the base station,thus improving the network life cycle and throughput.2.To address the problems of low coverage rate of monitoring area and long moving distance of nodes in the process of coverage optimization in wireless sensor networks(WSNs),a multi-strategy improved sparrow search algorithm for coverage optimization in a WSN(IM-DTSSA)is proposed.Firstly,Delaunay triangulation is used to locate the uncovered areas in the network after random deployment of static nodes.And the initial population of the sparrow search algorithm is selected from these locations,which can improve the convergence speed and search accuracy of the algorithm.Secondly,the quality and quantity of the explorer population in the sparrow search algorithm are optimized by the non-dominated sorting algorithm,which can improve the global search capability of the algorithm.Finally,a two-sample learning strategy is used to improve the follower position update formula and to improve the ability of the algorithm to jump out of the local optimum.Simulation results show that the IM-DTSSA algorithm can effectively balance the coverage rate of the target area and the moving distance of nodes,which is essential for WSNs coverage optimization.Simulation results show that the algorithm can effectively balance the coverage of the target area and the node movement distance,reduce the cost of the network deployment phase,and improve the overall network performance.Figure[22] Table[7] Reference[82]...