Localization algorithm based on maximum a posteriori in wireless sensor networks

Kezhong LU*, Xiaohua XIANG, Dian ZHANG, Rui MAO, Yuhong FENG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

22 Citations (Scopus)


Many applications and protocols in wireless sensor networks need to know the locations of sensor nodes. A low-cost method to localize sensor nodes is to use received signal strength indication (RSSI) ranging technique together with the least-squares trilateration. However, the average localization error of this method is large due to the large ranging error of RSSI ranging technique. To reduce the average localization error, we propose a localization algorithm based on maximum a posteriori. This algorithm uses the Baye's formula to deduce the probability density of each sensor node's distribution in the target region from RSSI values. Then, each sensor node takes the point with the maximum probability density as its estimated location. Through simulation studies, we show that this algorithm outperforms the least-squares trilateration with respect to the average localization error.
Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalInternational Journal of Distributed Sensor Networks
Issue number1
Early online date15 Dec 2011
Publication statusPublished - 1 Jan 2012
Externally publishedYes

Bibliographical note

This paper was supported by the National Natural Science Foundation of China (Grant no. 61003272, no. 61033009, no. 61170076, and no. 61103001), the Guangdong Natural Science Foundation (Grant no. 10351806001000000), and the Shenzhen Science and Technology Foundation (Grant no. JC201005280408A and JC2009D3120046A).


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