Fast Localization With Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements

Najarro, Lismer Andres Caceres; Song, Iickho; Tomic, Slaviša; Kim, Kiseon

doi:10.1109/lcomm.2020.3016710

Cited by 28 publications

(9 citation statements)

References 11 publications

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“…To solve (2), several approaches have been proposed and refined including those based on the linear least squares [24], convex optimization [25] and unscented transformation [26]. Recently, approaches based on evolutionary algorithms are gaining attention because they do not approximate (3) and tend to provide competitive localization accuracy [2], [3], [11].…”

Section: B Motivations and Key Contributionsmentioning

confidence: 99%

“…Over the past years, several evolutionary algorithms have been developed such as the differential evolution (DE) [6], genetic algorithm [7], particle swarm optimization [8], and ant colony optimization [9]. Among them, the DE is known to provide excellent solutions to several complex problems with reasonable robustness and computational demand [10], [11]. Despite the robustness and relative simplicity of the DE, however, it has also been shown that the accuracy of the DE and its variants are highly dependent on their control parameters [12].…”

Section: Introductionmentioning

confidence: 99%

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Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

2022

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The differential evolution (DE) is a well known population-based evolutionary algorithm that has shown capabilities for solving real-world problems such as resource allocation, multicast routing, and localization of target nodes. However, the accuracy of the DE, like other evolutionary algorithms, depends on the settings of its control parameters. The localization of target nodes is highly nonlinear and multi-modal, which may trap the DE in a local optimum. A local optimum may be avoided by a proper selection of the control parameters. One of the key control parameters is the population size (PS), which affects directly the localization accuracy and computational complexity. Finding an adequate PS throughout the evolution process is a challenging task. Even if an adequate PS is found it may not be the adequate PS anymore when the scenario of a problem changes. Although several approaches have been proposed for adapting the PS, they have not been evaluated when solving the localization problem. In this paper, a comprehensive comparison in terms of accuracy and computational demand is conducted among the stateof-the-art PS adaptation techniques when employed with the DE for solving the localization problem of target nodes in various scenarios. We also propose three new PS adaptation techniques, namely, exponential, parabolic, and logistic reduction. The results from extensive numerical simulations show that, after setting the initial PS properly, there is no technique that outperforms the others in practically all the scenario of the localization problem. Additionally, the DE with the proposed techniques provides competitive localization accuracy with considerably less computational complexity. Specifically, The proposed approaches reduce the computational demand by approximately 50% over the standard DE in all the scenarios considered here.

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Section: B Motivations and Key Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

2022

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“…On the contrary, algorithms based on semidefinite programming and/or second order cone programming are known to provide a higher localization accuracy, but at the cost of a higher computational demand [80]. Recently, localization algorithms based on meta heuristic approaches are getting considerable attention due to the fact that they provide a more reasonable trade-off between localization accuracy and computational complexity [226][227][228]. Based on these works, it seems plausible to expect that meta heuristic approaches could provide interesting results for dealing also with the practical limitations with a considerably low computational complexity.…”

Section: B Low Computational Complexitymentioning

confidence: 99%

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Najarro¹,

Song²,

Kim³

2021

Preprint

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With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

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“…Nowadays, the demand for indoor positioning is increasing, and wireless positioning systems in different environments have been widely studied. Indoor positioning systems based on wireless signals have become a research hotspot in last several years [4][5][6][7]. Indoor positioning is broadly classified into two categories according to the positioning principle: infrastructure-less and infrastructurebased, both of which have achieved good research results.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter

Cao

Feng

et al. 2022

IJGI

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In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms.

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Fast Localization With Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements

Cited by 28 publications

References 11 publications

Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Indoor Positioning Algorithm Based on Reconstructed Observation Model and Particle Filter

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