Application of Supervised Learning Approach for Target Localization in Wireless Sensor Network

Jondhale, Satish R.; Shubair, Raed M.; Labade, R. P.; Lloret, Jaime; Gunjal, Pramod R.

doi:10.1007/978-3-030-40305-8_24

Cited by 21 publications

(10 citation statements)

References 37 publications

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“…This means that the calculated distances of u 4−1 and b 3 coincide with the measured value ρ 3 . Refer to the cosine position result [31][32][33][34][35].…”

Section: Positioning Using Salam Algorithmmentioning

confidence: 99%

3D Localization for Mobile Node in Wireless Sensor Network

Javed

Tang

Saleem

et al. 2022

Wireless Communications and Mobile Computing

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Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.

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“…This means that the calculated distances of u 4−1 and b 3 coincide with the measured value ρ 3 . Refer to the cosine position result [31][32][33][34][35].…”

Section: Positioning Using Salam Algorithmmentioning

confidence: 99%

3D Localization for Mobile Node in Wireless Sensor Network

Javed

Tang

Saleem

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…We know that artificial neural network (ANN) once trained with appropriate dataset can deal with almost any non-linear system dynamics. 17,18 The authors in Zhou et al 19 designed a novel hybrid hypothesis test based on the idea of asymptotic relative efficiency (ARE), which takes into account the signal distributions from different Wi-Fi access points. In this research work, the Jarque-Bera (JB) test is utilized to perform the normality test on the Wi-Fi signal distribution, and then to obtain contribution degree of each access point, the chi-square automatic interaction detection (CHAID) approach is adopted.…”

Section: Related Workmentioning

confidence: 99%

“…So the localization error for kth time instance can be computed by averaging these two error values. Then, the overall localization error (we named it as average localization error) for total simulation period T can be computed using equation (17). Lower the values of these two parameters, high will be the target localization (or tracking) accuracy.…”

Section: System Design and Assumptions Of Tcgrnn-based Landt Systemmentioning

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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“…Position determination and position prediction analysis are the major tasks to be performed in this section. In WSNs, during occurrence of uncertain events like change of motion, slowing down or acceleration of moving target, the tracking process is challenging [16]. Loss of target or false target may occur if matching of actual moving model and the target maneuver model is not performed.…”

Section: Proposed Workmentioning

confidence: 99%

June 2020

2020

JSWS

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For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.

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Application of Supervised Learning Approach for Target Localization in Wireless Sensor Network

Cited by 21 publications

References 37 publications

3D Localization for Mobile Node in Wireless Sensor Network

3D Localization for Mobile Node in Wireless Sensor Network

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

June 2020

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