Node distribution-based localization for large-scale wireless sensor networks

Han, Sangjin; Lee, Sung‐Jin; Lee, Sanghoon; Park, Jongjun; Park, Sangjoon

doi:10.1007/s11276-009-0210-1

Cited by 27 publications

(10 citation statements)

References 14 publications

(18 reference statements)

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“…A large number of research is conducted on localization for WSN. The existing localization algorithms of WSN are categorized into two main classes: range-based 26,27 and range-free. 28,29 Range-based techniques use distance or angle to estimate their location, whereas range-free techniques only use connectivity information between unknown nodes and landmarks.…”

Section: Related Workmentioning

confidence: 99%

On precise path planning algorithm in wireless sensor network

Javed

Khan

et al. 2018

International Journal of Distributed Sensor Networks

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Wireless sensor network is a collection of small devices called sensors nodes, which are deployed in the sensing field to monitor physical and environmental information. Location information of sensor node is a critical issue for many applications in wireless sensor network. The main problem is to design a path for a mobile landmark to maximize the location accuracy as well as to reduce energy consumption. Different path planning schemes have been proposed for localization.Here, this study focused only on static path planning scheme. In this article, the performance of five static path planning schemes is evaluated, namely, random way point, Scan, D-Scan, Hilbert, and Circles based on three parameters such as location error ratio, energy consumption, and number of references. Network simulator-2 is used as a simulation tool. Simulation scenarios with three node densities are used in this research study such as sparse node density, medium node density, and dense node density. The analysis of simulation results concludes that random way point has higher performance efficiency compared to rest of the static path planning algorithms concerning location error ratio (accuracy), energy consumption, and number of references in medium and dense node density scenarios. Hilbert performance was found good only in sparse node density scenario.

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Section: Related Workmentioning

confidence: 99%

On precise path planning algorithm in wireless sensor network

Javed

Khan

et al. 2018

International Journal of Distributed Sensor Networks

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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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“…In the second step, it excludes neighbours with a high biasing effect to construct a subset of references that require fewer iterations to converge to an accurate position estimate. Han et al (2010) modify the dwMDS algorithm by simplifying the computation and reducing the processing time. Moore et al (2004) also used this approach.…”

Section: Noisy Distance Estimatementioning

confidence: 99%

Localised information fusion techniques for location discovery in wireless sensor networks

Abu‐Mahfouz

Hancke

2018

IJSNET

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Many wireless sensor network (WSN) applications and techniques require sensor location to work correctly. Information fusion in WSNs is crucial for location discovery and many information fusion techniques have been used in several location-discovery algorithms. In this paper, we provide an overview of information fusion techniques used by localisation algorithms to enhance and simplify the position computation process. Furthermore, we analyse several approaches that can be used by localised information fusion algorithms and discuss how these approaches enhance the performance of information fusion and make information fusion play a leading role in localisation algorithms. This paper provides an introduction to different localised information fusion methods for node location discovery in WSNs and could be used as a starting point for implementing existing proposals or designing new techniques.

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Node distribution-based localization for large-scale wireless sensor networks

Cited by 27 publications

References 14 publications

On precise path planning algorithm in wireless sensor network

On precise path planning algorithm in wireless sensor network

Mobile geolocation techniques for indoor environment monitoring

Localised information fusion techniques for location discovery in wireless sensor networks

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