Filtering Based Mobile Node Licalization In Wireless Sensor

Node localization is one of the key technologies in wireless sensor network, inthis paper, we summarize the current mobile node localization algorithms based onMonte Carlo localization(MCL) idea. According to the little samples in the regionwhere the value of posterior density distribution, the algorithm does not get desiredeffect, an improved algorithm called MCL weighted by similarity(MCWS) isproposed. Adopting the mobile node’s location based on the received signal strengthindicator(RSSI)as the new sampling center, MCWS can optimize the sampling area ofMCL. The signal values are stored as a target sequence, and by comparing thesimilarity between samples’sequences and the target sequence, samples can be filtered.Also the similarity values are used as the weighted standards to calculate coordinateof the mobile node. Extensive simulation results confirm that the MCWS algorithmreduces the localization error by1%~10%under different density of beacon nodesand by3%~4%under different maximum speed of mobile node, respectively.Monte Carlo Localization(MCL) has a decisive role for the mobile nodes’localization in Wireless Sensor Networks(WSN). In order to improve the positioningaccuracy, an improved algorithm called Sequence Correlation Optimized Monte CarloLocalization(SCMCL) is proposed. Adopting the mobile node’s location based on thereceived signal strength indicator(RSSI)as the new sampling center, SCMCL canoptimize the sampling area of MCL and etc. The signal values are stored as a targetsequence, and by comparing the correlation values between samples’ sequences andthe target sequence, samples can be filtered out. Also the correlation values are usedas the weighted standards to calculate coordinates of the mobile nodes. Extensivesimulation results confirm that the new localization approach outperforms MCL andetc. The SCMCL algorithm reduces the localization error by about10%under thesame density of beacon nodes and the maximum speed of mobile nodes.