Abstract
The physical constraints in wireless sensor networks can often be a significant compromising factor to their effective performance. To improve energy efficiency, there has been increasing research efforts that investigate employing predictive sensing mechanisms where adaptive clustering schemes and intelligent predictive algorithms are incorporated in data acquisition and transmission. There seems, however, little treatment in the literature on performance modelling of the predictive sensing scenarios. In this paper we examine a few light-weight data prediction algorithms operating in a synchronized cluster setting on some real-world environmental sensing data streams. Besides their capabilities in transmission reduction, we look at the relevant traffic modelling of the sensing scheme. Some empirical results obtained from real-world environment monitoring data are presented and discussed.