A model-free localization method for sensor networks with sparse anchors

Maggie Cheng, Wei Biao Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper considers the problem of sensor node localization, where a total of n anchor nodes are used to determine the locations of other nodes based on the received signal strengths. Challenges arise when anchor nodes are sparse and locations of them are not at grid positions. A range-based machine learning algorithm is developed to tackle the challenges. Instead of using samples to calibrate the parameters of a chosen signal model, we use machine learning to estimate the signal propagation function and its parameters at the same time. It overcomes the model dependency issue of existing range-based algorithms, and avoids the insufficient support issue of support vector machine methods. Simulation results show that the proposed algorithm has good adaptability to different signal characteristics, network deployment, and device variability. It significantly outperforms existing methods, especially when the anchor nodes are sparsely and irregularly deployed.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Communications, ICC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479966646
DOIs
StatePublished - Jul 12 2016
Event2016 IEEE International Conference on Communications, ICC 2016 - Kuala Lumpur, Malaysia
Duration: May 22 2016May 27 2016

Publication series

Name2016 IEEE International Conference on Communications, ICC 2016

Other

Other2016 IEEE International Conference on Communications, ICC 2016
CountryMalaysia
CityKuala Lumpur
Period5/22/165/27/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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