Open AccessLocation prediction optimisation in WSNs using Kriging interpolation
Author(s) -
Ali Arshad,
Ikpehai Augustine,
Adebisi Bamidele,
Mihaylova Lyudmila
Publication year - 2016
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
ISSN - 2043-6394
DOI - 10.1049/iet-wss.2015.0079
Subject(s) - kriging , interpolation (computer graphics) , computer science , data mining , artificial intelligence , mathematics , machine learning , motion (physics)
Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This study presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, the degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that the proposed algorithm delivers approximately 98% prediction accuracy.