Gradient Descent Localization in Wireless Sensor Networks

Alwan, Nuha A. S.; Hussain, Zahir M.

doi:10.5772/intechopen.69949

Cited by 10 publications

(10 citation statements)

References 23 publications

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“…Ideally, this is the optimum step size determination. However, it has been proved in [18] that the BB method cannot be implemented successfully for WSN localisation since its accuracy is largely dependent on the exact communication of TOA measurements between anchors and the central node in order to accurately compute the needed position estimates and gradients, a requirement that is practically unrealisable under link failure and noisy conditions.…”

Section: Gd Localisation For Wsnsmentioning

confidence: 99%

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

Alwan

Hussain

2018

IET wirel. sens. syst.

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Energy efficiency is an important requirement in wireless sensor networks in order to achieve cost-effectiveness and practical implementation. The present work deals with the problem of minimising node power consumption in the context of moving-node localisation and tracking. Time-of-arrival measurements are sent from anchor nodes to a powerful, usually sophisticated, central node, called the fusion centre, where all computations are performed. Low data rates are desirable to economise on node energy but result in sub-optimal localisation accuracy. It makes sense, therefore, to sample measurements at a low data rate while interpolating the data stream at the fusion centre to improve localisation. The localisation error is remarkably reduced and energy efficiency increased by using this conventional sample rate conversion technique. A further improvement in terms of localisation error is achieved using compressive sensing (via random sampling and interpolation), whereby the localisation error function is shown to decrease with higher-average random sampling periods.

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Section: Gd Localisation For Wsnsmentioning

confidence: 99%

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

Alwan

Hussain

2018

IET wirel. sens. syst.

Self Cite

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“…is transformed between sensor nodes in the form of packets and receives meaningful information [1], [2]. The sensor nodes are required to be located.…”

Section: Introductionmentioning

confidence: 99%

“…(1) z k = h(x k ) + v k (2) where the function f is to calculate the predicted state from the prior estimated state and h can be used to determine the previously expected calculation. However, f and h cannot be used directly for the covariance, instead, a partial derivative (Jacobian) matrix is calculated.…”

Section: Introductionmentioning

confidence: 99%

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

et al. 2020

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Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization. INDEX TERMS Extended Kalman filter, localization, particle filter, robot, unscented Kalman filter, wireless sensor networks.

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“…This means that our paper relates to the existing literature on localizing sensors in space using noisy measurements of distances from landmarks or beacons. To the best of our knowledge, it is possible to do so using three different approaches: triangulation, where the position is determined through measuring the angles between the sensing device and the known landmarks (see, e.g., in [ 24 ]); trilateration, where the position is determined through measuring the distances between the device and the landmarks (see, e.g., in [ 25 , 26 ]); triangulateration, a strategy that combines both of the above (see, e.g., in [ 27 , 28 ]). …”

Section: Introductionmentioning

confidence: 99%

“…trilateration, where the position is determined through measuring the distances between the device and the landmarks (see, e.g., in [ 25 , 26 ]);…”

Section: Introductionmentioning

confidence: 99%

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

Alhashimi

Magnusson

Knorn

et al. 2020

Sensors

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We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

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Gradient Descent Localization in Wireless Sensor Networks

Cited by 10 publications

References 23 publications

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions

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