Improved TOA-Based Localization Method with BS Selection Scheme for Wireless Sensor Networks

Go, Seungryeol; Chong, Jong‐Wha

doi:10.4218/etrij.15.0114.1251

Cited by 31 publications

(17 citation statements)

References 23 publications

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“…TOA localization requires synchronization between a source and receivers. The authors of proposed a base station selection scheme to reduce localization error in real indoor experiments based on TOA localization. Although TDOA does not require synchronization, it does require additional hardware devices .…”

Section: Introductionmentioning

confidence: 99%

Multiregional secure localization using compressive sensing in wireless sensor networks

2019

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Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.

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Section: Introductionmentioning

confidence: 99%

Multiregional secure localization using compressive sensing in wireless sensor networks

2019

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“…The cause of the systematic error is in Non Line-of-Sight conditions, multipath and clock off (Go and Chong, 2015;Sahinoglu, et al, 2008). In this paper, we call a difference between a true distance and a measured distance a bias, and focus on the bias.…”

Section: Introductionmentioning

confidence: 99%

Indoor Location Estimation Based on Toa Data and Bias Estimation Using Gamma Regression

Yoshida

Sakumura

Kamakura

2018

Journal of the Japanese Society of Computational Statistics

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We aim at improving the accuracy of indoor position estimation through a statistical approach. In this study, we propose a position estimation method based on Time-of-Arrival (ToA). ToA data are often useful. However, ToA data include a positive bias due to the reflection of radio waves. Therefore, it is difficult to estimate the TAG position from ToA data directly without an accurate bias correction. In this paper, we propose a maximum likelihood estimation method for the TAG position using gamma regression and a rotated distribution, and we show that the estimation with bias correction is more accurate than the estimation without bias correction. In addition, we show that our method also provides a confidence region for the TAG position.

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“…Some studies [1]- [2] tried to change the initial deployment of the network by mobile nodes; some other approaches [3]- [5] utilized MSs to collect data in order to enhance the sensing coverage, connectivity, and network lifetime as well. However, there has been little research work on WSN closed operating cycles to find an optimal solution for improving the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

Nguyen¹,

Nguyen²,

Le³

2017

ETRI Journal

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Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms.

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Improved TOA-Based Localization Method with BS Selection Scheme for Wireless Sensor Networks

Cited by 31 publications

References 23 publications

Multiregional secure localization using compressive sensing in wireless sensor networks

Multiregional secure localization using compressive sensing in wireless sensor networks

Indoor Location Estimation Based on Toa Data and Bias Estimation Using Gamma Regression

Efficient Approach for Maximizing Lifespan in Wireless Sensor Networks by Using Mobile Sinks

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