AWCL: Adaptive Weighted Centroid Localization as an efficient improvement of coarse grained localization

Behnke, Ralf; Timmermann, Dirk

doi:10.1109/wpnc.2008.4510381

Cited by 63 publications

(34 citation statements)

References 5 publications

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“…Fang and Li proposed a distributed adaptive quantization approach [15], that is, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors under the condition that sensors successively broadcast their quantized data. AWCL is to compensate too high Link Quality Indicator (LQI) values and give more influence to differences between LQIs instead of nominal values [16]. The adaptive character of AWCL leads to a small localization failure.…”

Section: Related Workmentioning

confidence: 99%

Adaptive data aggregation scheme in clustered wireless sensor networks

Chen

Mineno

Mizuno

2008

Computer Communications

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Section: Related Workmentioning

confidence: 99%

Adaptive data aggregation scheme in clustered wireless sensor networks

Chen

Mineno

Mizuno

2008

Computer Communications

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“…Weighted centroid localization (WCL), as proposed by Blumenthal et al [8], assigns a weight to each of the receiver coordinates, as inversely proportional to either the known transmitterreceiver (T-R) distance or the link quality indicator available in ZigBee/IEEE 802.15.4 sensor networks [9]. Behnke and Timmermann [10] extend the WCL mechanism for use with normalized values of the link quality indicator. Schuhmann et al [11] conduct an indoor experiment to determine a set of fixed parameters for an exponential inverse relation between T-R distances and the corresponding weights used with WCL.…”

Section: Related Workmentioning

confidence: 99%

Relative Span Weighted Localization of Uncooperative Nodes in Wireless Networks

Laurendeau

Barbeau

2009

Wireless Algorithms, Systems, and Applications

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Abstract. Increasingly ubiquitous wireless technologies require novel localization techniques to pinpoint the position of an uncooperative node, whether the target be a malicious device engaging in a security exploit or a low-battery handset in the middle of a critical emergency. Such scenarios necessitate that a radio signal source be localized by other network nodes efficiently, using minimal information. We propose two new algorithms for estimating the position of an uncooperative transmitter, based on the received signal strength (RSS) of a single target message at a set of receivers whose coordinates are known. As an extension to the concept of centroid localization, our mechanisms weigh each receiver's coordinates based on the message's relative RSS at that receiver, with respect to the span of RSS values over all receivers. The weights may decrease from the highest RSS receiver either linearly or exponentially. Our simulation results demonstrate that for all but the most sparsely populated wireless networks, our exponentially weighted mechanism localizes a target node within the regulations stipulated for emergency services location accuracy.

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“…Actually, the centroid method is highly suitable in WSNs because of its easy implementation without requiring too much computing resources. However, due to the multipath effect and the shadow fading, which are always present in signal propagation in an indoor environment, the performance of centroid method is unsatisfactory [4].…”

Section: Introductionmentioning

confidence: 99%

Improved indoor tracking based on generalized t-distribution noise model

Liu

Yin

Khuen

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Abstract-The use of wireless sensor networks for indoor localization application has emerged as a significant area of interest over the last decade, primarily motivated by its low cost and convenient deployment. The weighted centroid localization algorithm is a suitable positioning technique in a wireless sensor network due to its easy implementation. However, the performance of this method is easily affected by outliers and interference in the measurement of radio signal strength. In order to overcome this limitation, a more robust ARMA filter using generalized t-distribution noise model based on influence function approach is proposed. A hardware prototype was implemented to demonstrate that the ARMA filter could improve system performance, especially when dealing with the case of measurement outliers.

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AWCL: Adaptive Weighted Centroid Localization as an efficient improvement of coarse grained localization

Cited by 63 publications

References 5 publications

Adaptive data aggregation scheme in clustered wireless sensor networks

Adaptive data aggregation scheme in clustered wireless sensor networks

Relative Span Weighted Localization of Uncooperative Nodes in Wireless Networks

Improved indoor tracking based on generalized t-distribution noise model

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