Research On Coverage Optimization Of Wireless Sensor Networks Based On Improved Swarm Intelligence Algorith

The coverage of a wireless sensor network determines whether it will function properly in the future.Because the layout area is usually quite remote and harsh,the device is often used to spray the sensor randomly,but it is easy to cause uneven distribution of nodes.In order to solve this problem,it is usually necessary to redeploy it.In this thesis,two-dimensional and three-dimensional wireless sensor network coverage optimization is studied by computing and optimizing the redeployment location using group intelligence algorithm.The main research contents are as follows:(1)Aiming at the problems of insufficient coverage and too many nodes involved in the existing algorithms,a hybrid gray wolf-adaptive butterfly algorithm is proposed.Firstly,the grey wolf algorithm is integrated into the global search of butterfly algorithm,and the internal optimization of population is added into the inter-population optimization to speed up the convergence and improve the optimization ability.Then,the adaptive switching probability is improved,and the current optimization method is determined according to the current actual iteration situation,so that the computing resources are reasonably utilized and the search speed is accelerated.Finally,an improved feedback mechanism is added to prevent falling into local convergence,and compared with other algorithms,it is proved that the improved algorithm can improve coverage with as few nodes as possible.(2)In order to achieve better coverage,the coverage model is further improved on the premise of keeping the coverage basically unchanged.Multi-target coverage of two-dimensional wireless sensor networks can reduce the redundancy rate of nodes,save the network cost,shorten the moving distance of nodes,reduce energy consumption and prolong the network life in the future.From three aspects of optimal coverage,minimum energy consumption and minimum redundancy,the improved hybrid grey wolf-adaptive butterfly algorithm is used to optimize it.Compared with different algorithms in different scenarios,it is proved that the improved algorithm and model can effectively reduce node redundancy and energy consumption under the premise of keeping the coverage basically unchanged.(3)In order to be more suitable for practical application,the hybrid wireless sensor network including movable nodes and fixed points in three-dimensional scene is studied,and an improved sparrow algorithm is proposed.Firstly,the producer location updating formula of sparrow algorithm is improved,and the previous generation optimal population is added as a guide,and adaptive factors are added according to the search range of theoretical analysis to improve the convergence speed and optimization ability of the algorithm.Then,the scope of reconnaissance is expanded,the worst population is introduced to disturb the optimal population to prevent it from falling into local convergence,and the optimal population is introduced to guide it to speed up the search.Finally,an improved feedback mechanism is introduced to walk randomly to prevent local convergence.In different experimental scenarios,it is proved that the improved algorithm can effectively improve the coverage rate and the effect is stable.