—Localization in sensor networks deals with the estimation of the position of the sensor node in a network for a given incomplete and inaccurate pair-wise distance measurements. Such distance data may be acquired by a sensor node by communicating with neighboring nodes called anchor nodes whose positions are known apriori. This paper proposes a Kalman filtering based distance estimation algorithm for indoor wireless sensor networks. In this paper, the distance of the unknown node is computed based on the Received Signal Strength (RSS) measurements. The effect of path loss and attenuation in the wireless medium are also considered in this proposed algorithm. The distance error is minimized using one-dimensional Kalman filter. The number of iterations in Kalman filter is limited using Cramer Rao Bound (CRB) value. A real-time experimentation is carried out to get Received Signal Strength value in indoor environment using zigbee series 1 RF module along with the associated X-CTU software of Maxstream. The proposed algorithm is simulated in MATLAB version 7. From the simulation results it is found that the proposed distance estimation algorithm gives accurate results.
—Received signal strength, log normal shadowing model, ITU model, one-dimension Kalman estimator, cramer rao bound.
Authors are with Madras Institute of Technology, Anna University, Chennai, Tamilnadu, India (e-mail: email@example.com, firstname.lastname@example.org)
Cite: P. T. V. Bhuvaneswari and V. Vaidehi, "An Efficient Distance Estimation Algorithm for Indoor Sensor Network," International Journal of Computer Theory and Engineering
vol. 3, no. 6, pp. 797-801, 2011.