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Authors: Vinod Kumar Jain ; Shashikala Tapaswi and Anupam Shukla

Affiliation: Indian Institute of Information Technology & Management, India

ISBN: 978-989-8425-84-3

Keyword(s): Location aware services, User location and tracking, Wireless LANs, Dimensional reduction techniques, Locally Linear Embedding(LLE), Semi-supervised learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Due to vast applications of mobile devices and local area wireless networks, location based services are popularized and location information use has become important . The paper proposes a method based on Semisupervised Locally Linear Embedding for localization in indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so the Received Signal Strength (RSS) vector received by the mobile station has large dimensions (i.e.101). First we have used Locally Linear Embedding, a dimensional reduction technique to reduce the dimensions of data, and then we have used semi-supervised learning algorit hm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme is easy in training and implementation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm. (More)

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Paper citation in several formats:
Jain, V.; Tapaswi, S. and Shukla, A. (2011). SSLLE: SEMI-SUPERVISED LOCALLY LINEAR EMBEDDING BASED LOCALIZATION METHOD FOR INDOOR WIRELESS NETWORKS.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 138-146. DOI: 10.5220/0003676801380146

@conference{ncta11,
author={Vinod Kumar Jain. and Shashikala Tapaswi. and Anupam Shukla.},
title={SSLLE: SEMI-SUPERVISED LOCALLY LINEAR EMBEDDING BASED LOCALIZATION METHOD FOR INDOOR WIRELESS NETWORKS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={138-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003676801380146},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - SSLLE: SEMI-SUPERVISED LOCALLY LINEAR EMBEDDING BASED LOCALIZATION METHOD FOR INDOOR WIRELESS NETWORKS
SN - 978-989-8425-84-3
AU - Jain, V.
AU - Tapaswi, S.
AU - Shukla, A.
PY - 2011
SP - 138
EP - 146
DO - 10.5220/0003676801380146

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