Grey Model Prediction Based Monte Carlo Localization Boxed Algorithm for Wireless Sensor Network

Mobility makes node localization more difficult and influences localization accuracy greatly for Wireless Sensor Network (WSN). Static localization algorithms cannot perform well in mobile WSN. As a range-free localization algorithm for mobile WSN, Monte Carlo Localization Boxed algorithm (MCB) localizes the unknown node by exploiting the node’s mobility and calculating the mean value of current location samples. In MCB algorithm, current location samples are selected in sample boxes randomly and purposelessly. When the anchor nodes’ density of mobile WSN is small and after several iterative times, the samples would degrade rapidly to cause low localization accuracy. To address those problems, Grey Model Prediction based Mont Carlo Localization Boxed Algorithm (GMMCB) is proposed in this paper. In predication phase of GMMCB, several previous time intervals’ location samples are used to predict the current samples by using the method of Grey Model Prediction. More accurate samples are predicted to improve localization accuracy and reduce iteration times of resampling to save precious energy of sensor nodes. The simulation compares the localization effectiveness of GMMCB, MCB and Centroid algorithms to demonstrate the good performance of GMMCB.