Sensor Selection Heuristic in Sensor Networks

Abstract

Abstract. We consider the problem of estimating the location of a moving target in a 2-D plane. In this paper, we focus attention on selecting an appropriate 3 rd sensor, given two sensors, with a view to minimize the estimation error. Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the 3 rd sensor be selected based on three measures viz. (a) collinearity, (b) deviation from the ideal direction in which the sensor should be selected, and (c) proximity of the sensor from the target. We assume that the measurements are subject to multiplicative error. Further, we use least square error estimation technique to estimate the target location. Simulation results show that using the proposed algorithm it is possible to achieve near minimum error in target location.

Spread-based Heuristic for Sensor Selection in Sensor Networks

“…But if the collinearity coefficient is large, the location estimation error is likely to be small. These results have been discussed at length in our earlier work [5].…”

“…This scheme requires an a-priori knowledge of the location of the target. In our earlier work [5], we have considered the problem of selecting the nth sensor given (a) distance measurements from (n -1) sensors (these measurements are subject to multiplicative errors), and (b) an a-priori estimate of the target location. The nth sensor thus selected minimizes the area of intersection of error annulus.…”

“…With respect to sensor selection, several papers [5], [8], [9], [11], and [12] have used different approaches.…”

“…(a) additive, and (b) multiplicative. These are subsequently described in some detail (see also our earlier paper [5]). In this paper, we largely confine ourselves to using the multiplicative model for errors.…”

Impact of colinearity of sensors selected for location estimation

“…Particularly, we discuss a method of selecting three sensors that measure distance to the target. This is so because, experiments [5] show that distance measurements from at least three sensors at distinct locations are a must to compute location of the target. However, availability of distance measurement from a fourth sensor significantly reduces the estimation error and measurement from a fifth sensor provides only marginal improvement in the estimate.…”