Research On Mobile Node Localization Technology In Wireless Sensor Networks

As the basic technology of the Internet of things,Wireless Sensor Networks(Wireless Sensor Networks,WSNs)has become one of the hot spots of research.The wireless sensor network can connect the virtual world with the real scale in an unprecedented scale.Wireless sensor network in national security,environmental monitoring,traffic management,space exploration,disaster prevention,and other fields is of great application value.Node location is a key basis for wireless sensor networks.If an event is detected,it is necessary to know the location of the sensor node and the sense data without location information is meaningless.At present,the research on node location technology is mainly aimed at static wireless sensor networks with fixed location after node deployment.However,many applications in wireless sensor networks such as pastoral livestock monitoring and tracking,ecosystem monitoring and so on.The movement of nodes causes many existing Static network positioning algorithm does not apply.Therefore,it is of theoretical and practical value to study the mobile node localization technology of wireless sensor networks.Monte Carlo localization algorithm is a typical localization algorithm in WSNs mobile node location technology,which can effectively and robustly solve complex positioning problems.This method can be applied to WSNs to help solve the problem of mobile node location.However,traditional MCL-based approaches need to acquire a large number of samples to calculate to achieve good precision.The energy of one node is limited and can't last for a long time.Aiming to solve the problems,we proposed an improved algorithm IMCL(Improved MCL),in which we apply genetic algorithm to improve MCL in MSNs for localization.Besides,we also use interpolation operation to predict the velocity and angle.Which improves the sampling efficiency by reducing the scope from which the candidate samples are selected.Experimental simulation show that the proposed IMCL algorithm compared with MCL algorithm positioning time reduced by about 30%,positioning precision is increased by about 10%.The traditional Monte Carlo localization algorithm also has the problem that demand high anchor node density requirement issues to achieve high localization accuracy.However,the cost of the anchor node is much higher than that of the ordinary node.The more the number of anchor nodes,the more the cost of the network will be increased accordingly.Aiming to solve the problems,we proposed an improved algorithm EMCL(Enhanced MCL),in which we apply the Genetic Algorithm into Monte Carlo,based on the real anchor nodes,the virtual anchor nodes are generated by genetic crossover operation,which indirectly increases the density of anchor nodes.Besides,we also use interpolation operation to predict the velocity and angle.Which improves the sampling efficiency by reducing the scope from which the candidate samples are selected.The experimental results show that the accuracy of the proposed algorithm is improved by about 25% at the same anchor node density,and the better positioning results can be obtained when the anchor node density is low.