ITL-MEPOSA: Improved Trilateration Localization With Minimum Uncertainty Propagation and Optimized Selection of Anchor Nodes for Wireless Sensor Networks

Abstract

For wireless sensor networks, sensing data without location information are useless for future processing and decision-making systems. A wireless localization method can provide critical position information for the sensing data in the coverage field. Trilateration localization is applied widely in many systems due to its low cost and widespread availability. However, there are many negative factors that lead to low localization accuracy. Many improved versions of trilateration localization, including the weighted trilateration localization, the trilateration centroid localization, and other related methods, have been proposed to improve localization accuracy. However, these methods do not consider the influence mechanism of the negative factors, and the localization results are unsatisfactory. In this paper, considering the uncertainty propagation mechanism during trilateration localization, we propose an improved trilateration localization method with minimum uncertainty propagation and optimized selection of anchor nodes (ITL-MEPOSA). In this method, we consider the uncertainty propagation from distance estimation to localization calculation and evaluate the uncertainty propagation of each distance estimation result. Then, we utilize a single scan-sliding window with an optimized algorithm to select anchor nodes with minimum uncertainty propagation. Finally, based on the selected anchor nodes and their corresponding distance estimation results, an accurate localization result can be obtained through the least square criterion. The simulations and experimental results show that the proposed method can obtain high localization accuracy with acceptable efficiency.