An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network

Singh, Munesh; Khilar, Pabitra Mohan

doi:10.1007/s11276-015-1116-8

Cited by 30 publications

(10 citation statements)

References 27 publications

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“…Most sensor-network-based localization techniques use RSS measurements [37,38]. Four sensor-based localization configurations exist: (1) static sensor nodes and static anchor nodes [39], (2) mobile sensor nodes and static anchor nodes [40], (3) static sensor nodes and mobile anchor nodes [41], and (4) mobile sensor nodes and mobile anchor nodes [42]. The study in [43] surveys recent localization techniques considering wireless sensor networks and their fundamental limits, challenges, and applications.…”

Section: Dolphin (Distributed Objectmentioning

confidence: 99%

Mobile geolocation techniques for indoor environment monitoring

2020

KSII TIIS

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Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

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Section: Dolphin (Distributed Objectmentioning

confidence: 99%

Mobile geolocation techniques for indoor environment monitoring

2020

KSII TIIS

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“…[1][2][3] Although acoustic signal has higher propagation delay than radio frequency signal, it is used in underwater sensor network because radio frequency (RF) signal requires larger antenna and high power with high error rate, nonnegligible capture effect, and great path loss. [4][5][6] Because it is not fruitful to use conventional medium (i.e., RF signal) in underwater sensor network (UWSN), the conventional techniques for deployment, node estimation, and localization of sensors 7,8 are not applicable for UWSN. As a consequence, various types of new researches for UWSN have been performed, such as the development of autonomous underwater vehicles (AUV), upgradation of the deployment strategy of sensors, and localization techniques for the underwater sensor.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality determination of unknown deployed underwater sensor network (UWSN) using cost function

Afrin

Anower

Islam

2020

Int J Communication

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Summary This paper proposes a novel estimation technique for dimensionality of deployed underwater sensor network (UWSN). In order to obtain the dimension, probing requests are sent from two anchor nodes (i.e., receivers), which are placed inside the network through buoy, to sensor nodes. Then, the sensor nodes transmit Gaussian signal as feedback to the anchor nodes. The cross‐correlation function (CCF) is determined of these Gaussian signals, which varies according to network dimension. As reference, analytical CCF of five reference networks of different dimensions is determined. Thereafter, by comparing the CCFs of unknown and reference UWSN, cost function (CF) is obtained. The lowest value of CF attained by the specific combination of the one reference and the unknown network indicates that the dimension of these networks is identical. The dimension of two unknown deployed networks is investigated to demonstrate the feasibility of the proposed technique. From the illustrated results, it is justified that the new strategy can provide correct information about the dimension of the deployed network.

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“…Gustafsson and Gunna [30] used the difference between the estimated and the actual distance of the unknown and its surrounding anchor nodes to proposed a weighted centroid localization algorithm, it was also verified by ZigBee experiment. Singh and Khilar [31] designed a range-free location algorithm based on moving anchor node, but the progressive updating location of anchor node itself was ignored.…”

Section: Introductionmentioning

confidence: 99%

Localization of Unmanned Aerial Vehicle Operators Based on Reconnaissance Plane With Multiple Array Sensors

Zhen

2019

IEEE Access

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For the sake of positioning the illegal unmanned aerial vehicle operators, the paper proposes a direction of arrival (DOA) estimation algorithm based on the reconnaissance plane with multiple array sensors. First, the number of unmanned aerial vehicle signals is determined by information theory criteria. Then combined support vector regression, the direction of the operator is calculated according to some approximating function through training. Finally, the location can be estimated by integrating the DOAs acquired with the array sensors on the reconnaissance aircraft. This algorithm is convenient and fast to be realized, moreover, as a result of adopting super resolution and multiple kernel learning, it can locate numerous radio signals simultaneously and performs well in the circumstance that signals impinge on the sensor array with small-angle interval, as well as the conditions of small samples and low signal to noise ratio, besides, the algorithm also applies to the array which gain-phase inconsistency exists among the sensors. INDEX TERMS Sensor array, radio positioning, direction of arrival, support vector regression, gain-phase inconsistency.

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An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network

Cited by 30 publications

References 27 publications

Mobile geolocation techniques for indoor environment monitoring

Mobile geolocation techniques for indoor environment monitoring

Dimensionality determination of unknown deployed underwater sensor network (UWSN) using cost function

Localization of Unmanned Aerial Vehicle Operators Based on Reconnaissance Plane With Multiple Array Sensors

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