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.
The average localization error depends on the transmission range and average connectivity of the anchor nodes and lies between 1.5 m and 3 m. In REF a localization algorithm based on maximum a posteriori probability (MAP) and RSSI ranging is proposed.
Localization Algorithm Based on Maximum a Posteriori in Wireless Sensor Networks
International Journal of Distributed Sensor Networks
Computer Science