Study On Wireless Sensor Network Coverage Optimization Algorithm Based On Improved Sparrow Search Algorithm

The progress of wireless networks and intelligent sensor technology has led to the emergence of wireless sensor devices,These devices constitute wireless sensor networks(WSNs),which represent the most implemented elements in the Internet of things(IOT).Wireless sensor devices can be used in large-scale Internet of things applications.Smart devices can sense,communicate and process the collected sensing data,and then transmit it to the base station through the Internet for further analysis.Therefore,the issue of coverage maximization has attracted extensive attention.Coverage is one of the quality of service(Qo S)parameters of wireless sensor networks,which is closely related to power consumption.In addition to coverage,energy consumption and life are also Qo S parameters.Maximizing lifetime coverage means that the deployed sensor equipment can monitor the target area for a longer time with minimum energy while maintaining an appropriate coverage level.In the whole wireless sensor network,it is an extremely important task to maintain the required coverage level with the minimum number of sensor devices.The battery life of sensor equipment is limited and it is usually difficult to replace.Due to the performance constraints such as the power and stability of sensor nodes,it often leads to large coverage blind areas or node redundancy,which will shorten the service life of the network,reduce the reliability of the network,and cause a lot of resource waste in energy consumption and cost.Therefore,it is necessary to adjust and deploy the sensor nodes in wireless sensor networks adaptively,Make it more evenly distributed in the monitoring area and higher coverage.So as to increase the service life of the network and improve the reliability of the network.This is of great significance to the whole wireless sensor network.This paper mainly discusses the coverage optimization of WSNs in twodimensional plane and three-dimensional space,and designs different optimization algorithms according to different environments.Aiming at the two-dimensional plane,this paper proposes a single objective enhanced sparrow search algorithm to optimize the uneven coverage of WSN.The algorithm takes into account the nonlinear convergence factor,which can better balance the local and global search,and the characteristic that the Cauchy mutation operator jumps out of the local optimization,and takes the coverage as the optimization goal.For the three-dimensional surface,this paper selects two different simulation deployment environments: saddle terrain with gentle slope and multi peak terrain with harsh environment;Secondly,three objectives are considered: maximizing coverage,maximizing network lifetime and maximizing the number of data transmission;Finally,two improved multi-objective sparrow search algorithms are proposed,namely MOSSA-I and MOSSA-II.MOSSA-I algorithm combines reverse learning strategy and improved congestion calculation.MOSSA-II algorithm adds learning rate and external solution set strategy,and integrates the non-dominated sorting method based on reference points.The simulation results show that the proposed ESSA algorithm has better optimization effect in the single objective experiment.In the multi-objective experiment,MOSSA-I and MOSSA-II algorithms are significantly better than the algorithms in the existing literature in all related objectives.