Localization in the unknown environments and the principle of anchor placement

Suwansantisuk, Watcharapan; Lu, Henghui

doi:10.1109/icc.2015.7248698

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…AGV selects the four closest anchor nodes for estimating its location [30]. Random distribution of noise and path-loss parameters are taken into consideration during the location estimation of nodes in [31]. Furthermore, anchor placement was implemented according to WLAN infrastructure principles.…”

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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“…C. Refinement of x rts 0 , α rts and P rts The previous estimation of parameters x rts 0 , α rts and P rts can be further refined by using some of the ideas proposed in [33]. First, we initialize the location of the transmitter as in (11).…”

Section: B Bayesian Estimation Of α Rts and P Rtsmentioning

confidence: 99%

“…Then, we estimate the path loss exponent and the transmitted power as in (25). Next, we refine the position of the transmitter as [33], i.e.,…”

Section: B Bayesian Estimation Of α Rts and P Rtsmentioning

confidence: 99%

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Santos

Murillo-Fuentes

Djurić

2019

IEEE Trans. Signal Process.

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In this paper, we address the estimation of a timevarying spatial field of received signal strength (RSS) by relying on measurements from randomly placed and not very accurate sensors. We employ a radio propagation model where the path loss exponent and the transmitted power are unknown with Gaussian priors whose hyper-parameters are estimated by applying the empirical Bayes method. We consider the locations of the sensors to be imperfectly known, which entails that they represent another source of error in the model. The propagation model includes shadowing which is considered to be a zero-mean Gaussian process where the correlation of attenuation between two spatial points is quantified by an exponential function of the distance between the points. The location of the transmitter is also unknown and is estimated from the data. We propose to estimate time-varying RSS fields by a recursive Bayesian method and crowdsourcing. The method is based on Gaussian processes (GP), and it produces the joint distribution of the spatial field. Further, it summarizes all the acquired information by keeping the size of the needed memory bounded. We also present the Bayesian Cramér-Rao bound (BCRB) of the estimated parameters. Finally, we illustrate the performance of our method with experimental results on synthetic and real data sets.

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“…The deployment of wireless networks is mainly driven by communication demands [56], [57], [58], [59], [60], [61], [62], [63], [64]. In particular, the role of the network topology in the position error has been studied in [65], [66], [67] and node deployment strategies for localization have been developed in [68], [69], [70], [71], [72], [73], [74]. The design of node deployment strategies relies on optimizing a performance metric expressed as a function of the nodes' positions.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deployment Strategies for Network Localization With Assisting Nodes

Gómez-Vega,

Liu,

Gutiérrez

et al. 2024

IEEE Trans. on Mobile Comput.

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Location awareness is crucial for a variety of emerging applications. The accuracy of localization depends heavily on the spatial topology of the network, especially in complex and infrastructure-limited wireless environments. In these environments, assisting nodes can be deployed to achieve desirable performance. This paper presents efficient strategies for deploying assisting nodes to improve the localization accuracy of a target agent. Specifically, it provides a methodology to determine a finite set of candidate positions for the assisting nodes. Based on this methodology, we present a convex relaxation to select near-optimal positions for the assisting nodes and establish a theoretical limit on the localization accuracy provided by assisting nodes. We also propose an approximate dynamic programming algorithm to deploy assisting nodes with amenable complexity. A case study validates the proposed strategies and shows the benefits of deploying assisting nodes for accurate localization.

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Localization in the unknown environments and the principle of anchor placement

Cited by 9 publications

References 22 publications

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

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

Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes

Efficient Deployment Strategies for Network Localization With Assisting Nodes

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