Adaptive attenuation factor model for localization in wireless sensor networks

Purpose – The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms. Design/methodology/approach – The original signal propagation-based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately. Findings – The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN. Research limitations/implications – There is a need to have accurate location information of reference nodes. Practical implications – This is an effective low-cost solution for the localization of sensor nodes. Originality/value – An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.