RSD: A Metric for Achieving Range-Free Localization beyond Connectivity

Zhong, Ziguo; He, Tian

doi:10.1109/tpds.2011.105

Cited by 66 publications

(41 citation statements)

References 48 publications

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“…Obviously, MDS-SSD has better localization performance than MDS-Hop as the segments in Figure 13(b) are much shorter than that in Figure 13(a). However, considering the singularity issues in matrix decomposition, 26 it is not difficult to understand that MDS-Hop is not stable for linear or close-to-linear networks. In addition, hop-based distances may cause ambiguity problems in MDS-Hop while MDS-SSD can reduce that as far as possible.…”

Section: Methodsmentioning

confidence: 99%

Localization with signal-based signature distance

Chen¹,

Yin²,

Gao³

et al. 2017

International Journal of Distributed Sensor Networks

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Node localization plays a key and basic role for wireless sensor network. Existing range-free localization approaches suffer from precision limit of positioning, while range-based solutions may obtain good accuracy but pay high costs for ranging hardware. Instead of directly mapping received signal strength values into physical distances, a novel localization scheme with signal-based signature distance estimation is proposed for wireless sensor network. More specifically, we first quantify the near-far relationship between neighbor nodes through received signal strength comparison, and then the distance between non-neighbor nodes is calculated by an improved shortest path algorithm. Based on the obtained node distance, a relative map can be constructed by multidimensional scaling technology. Eventually, the locations of nodes can be acquired by virtue of Procrustes analysis method. For purpose of verifying the effectiveness of our design, several simulations are made in a sensor network with 200 randomly deployed nodes, and two experiments are implemented in real outdoor environments: a linear network and a two-dimensional grid network with 25 nodes. It is remarkable from results that the proposed scheme performs better in positioning and reduces localization errors by as much as 37% compared with state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Localization with signal-based signature distance

Chen¹,

Yin²,

Gao³

et al. 2017

International Journal of Distributed Sensor Networks

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“…In recent years, there have been some researches focusing on sub-hop proximity estimation between adjacent nodes, i.e., obtaining proximity information more finegrained than hop count to improve the accuracy of range-free localization algorithms Zhu et al 2012b;Zhong and He 2011]. Figure 1 illustrates the intuition to estimate sub-hop proximity between two adjacent nodes.…”

Section: Sub-hop Proximity Estimationmentioning

confidence: 99%

Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks

Zhang

Wang

et al. 2015

ACM Trans. Sen. Netw.

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Position information plays a pivotal role in wireless sensor network (WSN) applications and protocol/algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually rely on a unified per hop length (PHL) metric between nodes. But PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although PHL between different nodes might be greatly different, it exhibits significant locality, i.e., nearby nodes share similar PHL to anchors that know their positions in advance. Based on the locality of PHL, a novel distance estimation approach is proposed. Theoretical analyses show that the error of distance estimation in the proposed approach is only one fourth of that in state-of-the-art patterndriven scheme (PDS). An adaptive anchor selection is devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the adaptive anchor selection, a range-free localization algorithm named Selective Multilateration (SM) is proposed. Simulation results show that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared with state-of-the-art solutions, SM improves distance estimation accuracy by up to 57 percent and improves localization accuracy by up to 52 percent consequently.

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“…Some modified methods were proposed through the neighbor node information such as anchor information and relationship between node and anchor or topology structure to improve the DV-Hop method. Zhong and He [31] proposed a proximity metric called RSD (regulated signature distance) to capture the distance relationships among 1-hop neighboring nodes. This method can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy.…”

Section: Hop-count-based Localizationmentioning

confidence: 99%

A Survey of Localization in Wireless Sensor Network

Cheng

Zhang

et al. 2012

International Journal of Distributed Sensor Networks

228

154

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Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

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RSD: A Metric for Achieving Range-Free Localization beyond Connectivity

Cited by 66 publications

References 48 publications

Localization with signal-based signature distance

Localization with signal-based signature distance

Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks

A Survey of Localization in Wireless Sensor Network

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