On the optimal anchor placement in single-hop sensor localization

Miao, Qing; Huang, Baoqi

doi:10.1007/s11276-016-1424-7

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…Chepuri et al did not use CRLB, and anchor placement described as an elegant convex optimization problem. Miao and Huang studied optimal anchor placement in single-hop WSN localization [33]. The solution of the paper is related to the Fisher information matrix that applied to TOA.…”

Section: Anchor Placementmentioning

confidence: 99%

An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

Çavdar¹,

Günay²,

Ebrahimpour³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

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Section: Anchor Placementmentioning

confidence: 99%

An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

Çavdar¹,

Günay²,

Ebrahimpour³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…As in general anchors-based localisation approaches, the accuracy of area-based localisation is considerably affected by the number of anchors and their placement. While the problem of optimal anchor placement has been one of the problems that have attracted extensive research in the literature [7][8][9][10][11], the case of optimal placement for area-based localisation has been dealt with in a few studies given in Reference [10]. In that work, authors have focused on a special case of area-based algorithms where only uniform shapes (circles) have been considered and conducted an analytical study to derive the optimal pattern that achieves the highest localisation accuracy.…”

Section: Introductionmentioning

confidence: 99%

On optimal anchor placement for area‐based localisation in wireless sensor networks

Cheriet

Bachir

Lasla

et al. 2021

IET Wireless Sensor Systems

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We consider the problem of optimal anchor placement for area-based localisation algorithms with the goal of providing cost-effective, simple, and robust positioning in wireless sensor networks. Due to the high complexity of the problem, we propose two placement algorithms based on heuristics. The first, called genetic algorithm anchors placement (GAAP), is based on genetic algorithms meta-heuristic, and the second, called local search anchors placement (LSAP), is based on an intuitive heuristic inspired from search techniques used in quad-trees. For the evaluation of these algorithms, we built a simulation framework, which we made publicly available for the community, and compared their performance against a Brute force (BF) algorithm, and against RND, a random walk-inspired algorithm. Obtained results show that GAAP provides anchor placements that lead to a very high accuracy while keeping execution time drastically smaller compared to LSAP, BF, and RND. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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