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  • In Jour Smart Sensing And Intelligent Systems

 

Article | 01-September-2014

IMPROVED WEIGHTED CENTROID LOCALIZATION ALGORITHM BASED ON RSSI DIFFERENTIAL CORRECTION

Node localization is one of the key technologies of wireless sensor networks (WSN). Due to the indoor environment requires a high positioning accuracy, an improved algorithm based on RSSI is put forward. In terms of RSSI ranging, the error correction coefficient received from self-correcting locator beacon nodes, and then had been applied to distance solving from unknown nodes to the beacon nodes. In the choice of weights, the algorithm uses inverse distance and replaces the method of

En-jie Ding, Xin Qiao, Fei Chang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1156–1173

Article | 01-March-2016

A NEW THREE-DIMENSION SPATIAL LOCATION ALGORITHM OF WIRELESS SENSOR NETWORK

confidence interval of nodes perception radius is estimated. Secondly, satisfied tetrahedron is got by the intersections sphere coverage of beacon nodes in three-dimensional. Finally, destination node localization is calculated with weighted centroid in three-dimensional. Simulation results show that the proposed method can significantly reduce positioning errors in three-dimensional network environment in reality and can be widely applied to many fields.

Xiang Hua, Zhang Jinjin, Bin Lei

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 233–255

Article | 01-September-2016

A NOVEL HYBRID LOCALIZATION METHOD FOR WIRELESS SENSOR NETWORK

Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main

Wang Jun, Zhang Fu, Ren Tiansi, Chen Xun, Liu Gang

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1323–1340

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