Sensor networks are estimated to drive the formation of the future Internet, with stream reasoning responsible for analysing sensor data. Stream reasoning is defined as real time logical reasoning on large, noisy, heterogeneous data streams, aiming to support the decision process of large numbers of concurrent querying agents. In this research we exploited non-monotonic rule-based systems for handling inconsistent or incomplete information and also ontologies to deal with heterogeneity. Data is aggregated from distributed streams in real time and plausible rules fire when new data is available. The advantages of lazy evaluation on data streams were investigated in this study, with the help of a prototype developed in Haskell.
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