Adaptive attenuation factor model for localization in wireless sensor networks

Abstract

PurposeThe 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/approachThe 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.FindingsThe 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/implicationsThere is a need to have accurate location information of reference nodes.Practical implicationsThis is an effective low‐cost solution for the localization of sensor nodes.Originality/valueAn 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.

Received signal strength based localization of a wireless sensor network using particle swarm optimization

“…Hence the path loss parameters at the respective location can be calculated from the RSSI measurements in real time. In [53], the values of the path loss parameters are estimated in real time by using the RSSI between two anchor nodes. A selection scheme is proposed to select the anchor node that is close to the unknown node, so that the estimated values reflect the values at the area where the 22 unknown node is located more accurately.…”