The Research On Localization Of Anisotropic Wireless Sensor Networks Based On Swarm Intelligence

Wireless sensor network(WSN)is a multi-hop self-organizing network system formed by a large number of sensor nodes deployed in the monitoring area communicating with each other.Nodes establish network connection through wireless communication,and sense the surrounding environment through sensing devices to monitor the state information in the area.As one of the fundamental challenges of wireless sensor networks,node localization technology is widely used in many fields such as agriculture,environmental protection,building monitoring,medical health,and so on.A default assumption used in most current localization techniques is that the network topology is isotropic,that is,the sensor network has the same hop count information in all directions.However,in real applications,sensor nodes will be deployed in some complex network environments,which may have irregular geometric shape distribution,or uneven node distribution density.Or the geographical environment is complex and changeable.There are holes in this kind of network,which makes the shortest path between nodes bend,affects the calculation of ranging formula,and has a great impact on the method of relying on multi-hop to achieve localization.This kind of network is called anisotropic wireless sensor network.Therefore,the key problem to be solved in anisotropic wireless sensor networks is how to eliminate the impact of network holes on the hop count.In this paper,the swarm intelligence optimization algorithm based on optimization theory is applied to the field of anisotropic node localization,and the localization problem is transformed into the population search for the optimal solution,so as to achieve the purpose of improving the localization accuracy of the algorithm.The main research contents of this paper are as follows:Aiming at The problem that the classical Proximity Distance Mapping(PDM)localization algorithm has large error and high requirements for the proportion and specific position of anchor nodes,a PDM algorithm optimized by adaptive chaotic slime mold algorithm(PDM-TSMA)was proposed.Firstly,the adaptive chaos adjustment mechanism was applied to the Slime Mold Algorithm(SMA),and the Tent chaotic map was used to initialize the population to enhance the diversity of the population.Then,an adaptive chaotic oscillation factor was set up to balance the local and global search ability of the algorithm.Finally,TSMA and PDM algorithm were combined to further improve the localization performance of the localization algorithm through anchor node selection and feasible region restriction strategy.Simulation results show that under the same parameter conditions,the proposed algorithm has a significant improvement in convergence speed and positioning accuracy compared with other anisotropic positioning algorithms.Aiming at the poor population diversity of the slime mold algorithm,the poor population diversity,slow convergence speed and lack of mutation mechanism of the Slime mold algorithm,a multi-strategy optimization of the Slime mold algorithm(QOSMA)was proposed.Firstly,the elite opposition-based learning strategy is introduced to select the initial population to enhance the diversity and quality of the population.Secondly,the adaptive nonlinear oscillation factor was introduced to enhance the local search ability of the algorithm in the later stage and improve the convergence speed of the algorithm.Finally,the Cauchy mutation strategy was introduced to optimize the solution vector to further improve the global search ability and convergence speed of the algorithm.By using the proposed algorithm to search the unknown node coordinates globally,a new localization algorithm for Wireless sensor networks(QOSMA-WSNs)was proposed.Experimental results show that the proposed algorithm has better localization performance and optimization ability with similar complexity to other localization algorithms.