Abstract

The localization of a sensor in wireless sensor networks (WSNs) has now gained considerable attention. Since the transmit power and path loss exponent (PLE) are two critical parameters in the received signal strength (RSS) localization technique, many RSS-based location methods, considering the case that both the transmit power and PLE are unknown, have been proposed in the literature. However, these methods require a search process, and cannot give a closed-form solution to sensor localization. In this paper, a novel RSS localization method with a closed-form solution based on a two-step weighted least squares estimator is proposed for the case with the unknown transmit power and uncertainty in PLE. Furthermore, the complete performance analysis of the proposed method is given in the paper. Both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The relationships between the deterministic CRLB and the proposed stochastic CRLB are presented. The paper also proves that the proposed method can reach the stochastic CRLB.

Highlights

  • Wireless sensor networks (WSNs) have been widely used for monitoring and control in military, environmental, health and commercial systems [1,2,3,4]

  • A novel received signal strength (RSS) localization method based on a two-step weighted least squares (WLS) estimator is proposed for the case with the unknown transmit power and uncertainty in path loss exponent (PLE)

  • A novel RSS localization method based on a two-step WLS estimator is proposed for the case

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Summary

Introduction

Wireless sensor networks (WSNs) have been widely used for monitoring and control in military, environmental, health and commercial systems [1,2,3,4]. Various RSS-based location methods [16,17,18,19,20,21,22] have been proposed to consider the case where both the transmit power and PLE are unknown. Different estimators such as maximum likelihood (ML). A novel RSS localization method based on a two-step weighted least squares (WLS) estimator is proposed for the case with the unknown transmit power and uncertainty in PLE.

System Model
Closed-Form Solutions
Cramer-Rao Lower Bound
Performance Analysis
Simulation Results
Performance comparison different
The simulation
This implies
Comparison among the proposed stochastic the deterministic
Conclusions
Full Text
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