A Decentralized Semantic Reasoning Approach for the Detection and Representation of Continuous Spatial Dynamic Phenomena in Wireless Sensor Networks

In this paper, we propose a decentralized semantic reasoning approach for modeling vague spatial objects from sensor network data describing shape phenomena, such as forest fire, air pollution, traffic noise, etc. This is challenging problem it necessitates appropriate aggregation of and their update with respect to the evolution state phenomena be represented. Sensor are generally poorly provided in terms information. Hence, proposed starts building knowledge base integrating domain ontologies then uses fuzzy rules extract three-valued qualitative information expressing relative position each monitored phenomenon’s extent. The observed modeled using fuzzy-crisp type object made kernel conjecture part, which more realistic representation environmental phenomena. second step our computing techniques infer boundary detection vertices parts IF-THEN rules. Finally, present case study urban noise pollution monitoring by network, implemented Netlogo illustrate validity approach.