Lightweight multivariate sensing in WSNs

By Silva, J.M.C.; Carvalho, P.; Bispo, K.A.; Lima, S.R.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)



This paper proposes a self-adaptive sampling scheme for WSNs, which aims at capturing accurately the behavior of the physical parameters of interest in each specific WSN context yet reducing the overhead in terms of sensing events. The sampling scheme relies on a set of low-complexity rules capable of auto-regulate the sensing frequency in accordance with each parameter behavior. As proof-of-concept, based on real environmental datasets, we provide statistical indicators illustrating the added value of the proposed sampling scheme in reducing sensing events without compromising the estimation accuracy of physical phenomena.



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