NDLI: Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Content Provider	Springer Nature Link
Author	Jain, V. K. Tapaswi, Shashikala Shukla, Anupam
Copyright Year	2011
Abstract	Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for 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 RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm 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 has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.
Starting Page	879
Ending Page	893
Page Count	15
File Format	PDF
ISSN	09296212
Journal	Wireless Personal Communications
Volume Number	67
Issue Number	4
e-ISSN	1572834X
Language	English
Publisher	Springer US
Publisher Date	2011-10-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Location aware services User location and tracking Wireless LANs Dimensional reduction techniques Locally Linear Embedding (LLE) Semi-supervised learning Computer Communication Networks Signal, Image and Speech Processing Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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