Research on Localization Technology in Wireless Sensor Networks

Ma, Xinghua; Bing, Zhigang; Tang, YuQiu

doi:10.1007/978-3-642-21783-8_64

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…On the other hand, the range-free approach exploits connectivity data organised in neighbour tables or distance-vector metrics [4]. One the most popular ranged-free localisation protocol is DV-hop [5] which is highly adapted to multi-hop mesh topologies.…”

Section: Related Workmentioning

confidence: 99%

Built In-Stack Localisation Services with ALO

Bossche

Dalce

Val

2020

2020 9th IFIP International Conference on Performance Evaluation and Modeling in Wireless Networks (PEMWN)

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Section: Related Workmentioning

confidence: 99%

Built In-Stack Localisation Services with ALO

Bossche

Dalce

Val

2020

2020 9th IFIP International Conference on Performance Evaluation and Modeling in Wireless Networks (PEMWN)

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“…Localisation is a fundamental technology in wireless sensor networks (WSNs) [1, 2], because without location information, the data gathered by sensor nodes would be meaningless. As a popular outdoor localisation system, global positioning system (GPS) is not a good solution for indoor localisation because satellite signal cannot penetrate the concrete wall and roof [3–5]. Moreover, GPS technique requires mobile device communicate with remote satellites, thus consumes more energy [6].…”

Section: Introductionmentioning

confidence: 99%

RSS‐based indoor localisation using MDCF

Gui

Yang

Fang

et al. 2017

IET wirel. sens. syst.

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As a low-cost distance measurement method, received signal strength (RSS) is often used for indoor wireless sensor localisation. However, RSS values can be easily influenced by multi-path fading, noise and other environmental parameters. This decreases the accuracy and stability of estimated distance. To improve localisation accuracy, this study proposes a multiplicative distance-correction factor (MDCF) to counteract the inaccuracy of estimated distance. In the same indoor environment, the product of this CF and estimated distance is regarded as a good approximation of real distance between unknown node and an anchor node. Then, two location estimated methods based on MDCF (MDCF-grid and MDCF-particle swarm optimisation) are proposed. The experimental results confirm that the proposed location estimation methods can significantly improve localisation accuracy without extra hardware in practical indoor scenarios.

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