A Location Estimation Algorithm Based on RSSI Vector Similarity Degree. (31st August 2014)

• Record Type: Journal Article
• Authors: Shang, Fengjun; Su, Wen; Wang, Qian; Gao, Hongxia; Fu, Qiang
• Other Names: Shu Lei Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2014)
• Publication Date: 2014-08-31
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2014/371350
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurate location. Firstly, the distribution trend of RSSI is analyzed in this experiment and determined the loss model of signal propagation by processing experimental data. Secondly, in order to determine the distance between receiving nodes and sending nodes, Gaussian fitting is used to process specific RSSI at different distance. Moreover, the piecewise linear interpolation is introduced to calculate the distance of any RSSI. On the other hand, firstly, the RSSI vector similarity degree (R-VSD) is used to choose anchor nodes. Secondly, we designed a new localization algorithm which is based on the quadrilateral location unit by using more accurate RSSI and range. Particularly, there are two localization mechanisms in our study. In addition, the generalized inverse is introduced to solve the coordinates of nodes. At last, location error of the new algorithm is about 17.6% by simulation experiment.