Issues and challenges in RSSI based target localization and tracking in wireless sensor networks

Jondhale, Satish R.; Deshpande, Rajkumar S.; Walke, S. M.; Jondhale, Amruta S.

doi:10.1109/icacdot.2016.7877655

Cited by 26 publications

(12 citation statements)

References 10 publications

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“…Although the RSSI is a widely used metric for target L&T, it generally leads to high localization errors due to highly fluctuating nature of the RSSI measurements, and dynamicity in given radio frequency (RF) environment. 7,8 The trilateration technique is widely used in target L&T because of its implementation simplicity, and low computational complexity. [9][10][11] It can estimate the unknown target location with the help of its distances from three anchors (the nodes whose locations are known) using RSSI measurements.…”

Section: Introductionmentioning

confidence: 99%

Improved trilateration for indoor localization: Neural network and centroid-based approach

Jondhale¹,

Jondhale²,

Deshpande³

et al. 2021

International Journal of Distributed Sensor Networks

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Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.

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Section: Introductionmentioning

confidence: 99%

Improved trilateration for indoor localization: Neural network and centroid-based approach

Jondhale¹,

Jondhale²,

Deshpande³

et al. 2021

International Journal of Distributed Sensor Networks

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“…At present, high‐precision target tracking algorithms based on wireless sensor networks (WSNs) in noisy environments have become a research hotspot [1–3]. In many industrial and commercial fields, the application requirements of mobile target tracking are needed, e.g.…”

Section: Introductionmentioning

confidence: 99%

Noise‐aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter

Fang

Chen

2020

IET Radar, Sonar & Navigation

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“…RSS-based localization measures the attenuation degree of the signal strength, then constructs a form of map of the signal strength in the deployment area. This approach does not need clock synchronization but the RSSI values are generally corrupted by environment noise [14,16], especially in three-dimensional situation. In this paper, we use the distance-based localization algorithm for WSNs.…”

Section: Introductionmentioning

confidence: 99%

Rigid graph-based three-dimension localization algorithm for wireless sensor networks

Luo

Zhong

et al. 2018

Journal of Systems Engineering and Electronics

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This paper investigates the node localization problem for wireless sensor networks in three-dimension space. A distributed localization algorithm is presented based on the rigid graph. Before location, the communication radius is adaptively increasing to add the localizability. The localization process includes three steps: firstly, divide the whole globally rigid graph into several small rigid blocks; secondly, set up the local coordinate systems and transform them to global coordinate system; finally, use the quadrilateration iteration technology to locate the nodes in the wireless sensor network. This algorithm has the advantages of low energy consumption, low computational complexity as well as high expandability and high localizability. Moreover, it can achieve the unique and accurate localization. Finally, some simulations are provided to demonstrate the effectiveness of the proposed algorithm.

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Issues and challenges in RSSI based target localization and tracking in wireless sensor networks

Cited by 26 publications

References 10 publications

Improved trilateration for indoor localization: Neural network and centroid-based approach

Improved trilateration for indoor localization: Neural network and centroid-based approach

Noise‐aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter

Rigid graph-based three-dimension localization algorithm for wireless sensor networks

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