The Hybrid HMM for RSS-based Localization in Wireless Sensor Networks

DOI

10.2991/iccsee.2013.555How to use a DOI?

Keywords

Wireless Sensor Network, Localization, Received signal strength, Hidden Markov Model

Abstract

We propose a method of RSS-base localization in WSN (Wireless Sensor Network), called Hybrid HMM, to improve the stability of node localization based on RSS (Received Signal Strength). This model utilizes HMM (Hidden Markov Model) to take into account the time factor when receiving the RSS sequence, and converts the action of ranging into an operation of classification. For the received RSS used for localization, our Hybrid HMM will compare it with the preset RSS threshold value, and put the result into one of two categories for subsequent processing: If the received value is higher than the threshold value, the distance value will be drawn from the signal propagation model. If lower, the information will be obtained from a trained HMM. Experimental results show that the Hybrid HMM method can greatly improve the localization accuracy.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanhong Zang
AU  - Jinsong Wang
AU  - Lin Ling
AU  - Peizhong Lu
PY  - 2013/03
DA  - 2013/03
TI  - The Hybrid HMM for RSS-based Localization in Wireless Sensor Networks
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2211
EP  - 2216
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.555
DO  - 10.2991/iccsee.2013.555
ID  - Zang2013/03
ER  -