An Efficient Distance Estimation Algorithm for Indoor Sensor Network

Abstract

Abstract-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.Index Terms-Received signal strength, log normal shadowing model, ITU model, one-dimension Kalman estimator, cramer rao bound. I. INTRODUCTIONWireless sensor networks are fundamentally intended to provide information about the spatio-temporal characteristics of the observed physical world. Hence, it is necessary to associate sensed data with locations, making data geographically meaningful [1]. Hence, localization, a mechanism for autonomously discovering and establishing spatial relationships among sensor nodes, is of great importance in the development of wireless sensor networks. In most applications, the data reported by the sensors is relevant only if tagged with the accurate location of the sensor nodes. Thus knowledge of position of the node becomes inevitable.The determination of location information requires the estimation of distance between the beacon nodes and unknown nodes. The beacon node is a node that is aware of its location while unknown node is the one whose locations is to be determined. [3]. However, distributed RSS-based distance measurement technique is more suitable considering the power constraints in the design of wireless sensor network.In practice, due to resource constraints on the sensor nodes, additional hardware to perform distance measurements becomes infeasible. The commercially available zigbee RF modules provide the Received Signal Strength information through RSS Indicator (RSSI). In this paper, an efficient distance estimation algorithm based on RSS measurement is developed, which provides an accurate and cost-effective solution. Estimation of accurate distance is done by one-dimension Kalman filter estimator [4]. The number of iteration of the Kalman filter estimator is limited by Cramer-Rao Bound (CRB) [5].The rest of the paper is organized as follows. Section II provides an overview of existing approache...

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