TY  - JOUR
T1  - An Efficient Localization based on Relevance Vector Machine with
Glow-Worm Swarm Optimization for Wireless Sensor Networks
AU - Arun, M. AU - Manimegalai, P. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 2
SP  - 406
EP  - 414
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.406.414
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.406.414
KW  - Wireless sensor networks
KW  -localization
KW  -trilateration
KW  -triangulation
KW  -maximum likelihood
KW  -relevance vector machine
KW  -Glow-worm swarm behaviour based optimization algorithm
AB  - Wireless Sensor Networks (WSNs) have the prospect to become the most crucial technology of the
future. Based on the applications, there is a need to locate the physical location of sensor node to improve the
performance. This is known as localization problem. Some traditional localization algorithms are used but still
convergence problem exists. So, to solve the above problems and obtain an efficient location identification, a
system has been designed using machine learning and swarm intelligence. In this research, a Relevance Vector
Machine (RVM) with Glow-worm Swarm behaviour based optimization Algorithm (GSA) is proposed for efficient
localization. Here, the trilateration, triangulation and Maximum Likelihood (ML) based location discovery
process is focused. For high accurate localization, the proposed system considers the node density factor.
In this process, the node is in the overlapping region of circles considered as trilateration problem and it is
solved by RVM. The RVM is mainly used for splitting the anchor and overlapping region node and similarly
to find the weight for those nodes, so that, the processing time is reduced. After finding the innermost
intersection of a point, the GSA is used to update the archive based on the distance and geometric topology
constraints. The evaluation of proposed RVM-GSA localization is compared with Average Weight Based
Centroid Localization (AWBCL) algorithm with the help of MATLAB tool. The obtained result shows that the
proposed RVM-GSA algorithm is a promising scheme that can minimize the localization problem.
ER  -