Wireless Sensor Networks (WSNs) consist of spatially distributed wireless sensor nodes that cooperate with each other in order to monitor and collect data pertaining to physical or environmental conditions such as temperature, pressure, motion, sound, and other phenomena. The locations of the sensor nodes are not predetermined as they are usually randomly deployed in the region of interest. Therefore, algorithms that can compute the location of sensor nodes within a WSN are needed.
A new and efficient, accurate, and cost-effective algorithm, called iCCA-MAP, has been proposed for localizing mobile node(s) within a WSN. The proposed algorithm is based on the CCA-MAP algorithm, which applies an efficient nonlinear data mapping technique. Simulation results show that the localization error for both CCA-MAP and iCCA-MAP are similar. However, the computational time required for obtaining location estimates using iCCA-MAP is far smaller than that of the original CCA-MAP. Therefore, iCCA-MAP can be applied at closer time intervals in order to provide up-to-date estimates of the mobile node's location, which can result in a lower localization error. For a complete performance evaluation, iCCA-MAP has been compared to the well-known mobile node localization algorithms MCL and a variation of MCL, called Dual MCL. Simulation results show that iCCA-MAP outperforms MCL and Dual MCL by having a lower localization error when the minimum number of anchor nodes is used.