A New Lower Bound on Range-Free Localization Algorithms in Wireless Sensor Networks

Chan, Yiu Wing Edwin; Soong, Boon‐Hee

doi:10.1109/lcomm.2010.110310.101772

Cited by 22 publications

(14 citation statements)

References 11 publications

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“…Therefore, we can define the lower bounds in range-free schemes as the average distance in which the sensor node can move with the same connectivity information between the previous time and the current time. In this case, localization accuracy degenerates for range-free schemes [94].…”

Section: Discussion Of Future Work and Localization Issuesmentioning

confidence: 99%

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

et al. 2017

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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

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Section: Discussion Of Future Work and Localization Issuesmentioning

confidence: 99%

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

et al. 2017

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“…Rather, they estimate the distance between nodes based on the internode connectivity, the plurality of routing information exchange, and the overlapping areas [6]. Although range-free location methods have lower requirements for hardware and power consumption, the accuracy of localization is generally lower than that of the range-based methods [7,8]. In this paper, we employ an approach that follows the range-based methods.…”

Section: Introductionmentioning

confidence: 99%

A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Zhang

et al. 2016

International Journal of Distributed Sensor Networks

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Node localization is an important supporting technology in wireless sensor networks (WSNs). Traditional maximum likelihood estimation based localization methods (MLE) assume that measurement errors are independent of the distance between the anchor node and a target node. However, such an assumption may not reflect the physical characteristics of existing measurement techniques, such as the widely used received signal strength indicator. To address this issue, we propose a distance-based MLE that considers measurement errors that depend on distance values in this paper. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution is developed based on first-order optimal condition to improve the efficiency of search. In addition, a two-dimensional search method is also presented. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared to other range-based localization methods.

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“…Range-based localization method has relatively higher localization accuracy. It uses one or several distance measurement technologies to estimate the localization of the unknown node [7]. The existing techniques of distance measurement includes the received signal strength indicator (RSSI)time of arrival (TOA) time difference of arrival (TDOA) and angle of arrival (AOA).…”

Section: Introductionmentioning

confidence: 99%

Distance-based maximum likelihood estimation method for node localization in wireless sensor networks

Xu¹,

He²,

Zhang³

et al. 2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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Node localization is an important supporting technique in wireless sensor networks (WSNs). The traditional maximum likelihood estimation localization method (MLE) assumes that measurement errors are independent of the distance between anchor node and target node. However, the assumption may contradict with the physical characteristic of some existing measurement techniques, such as the widely-used received signal strength indicator. To address this issue, we propose a distance-based MLE considering the dependence of measurement errors on the distance. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution method is presented based on first order optimal conditional and to improve the search efficiency, a two-dimensional search method is also given. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared with other range-based localization methods

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A New Lower Bound on Range-Free Localization Algorithms in Wireless Sensor Networks

Cited by 22 publications

References 11 publications

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Distance-based maximum likelihood estimation method for node localization in wireless sensor networks

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