Event Classification and Intensity Discrimination for Forest Fire Inference With IoT

Simultaneously occurring random events are often reported by multiple nodes. However, the accuracy of the event detection at every node is dependent on the node’s relative position from the event, and hence, not reliable. Moreover, the factors influencing the event inference are so many, that the accuracy of such an event detection is compromised. Targeting the problem of accurate event inference in the detection of priority events, such as forest fire, a fuzzy rule-based method is proposed. Four parameters are identified for which fuzzified values are obtained by a membership function for every variable. A set of 256 rules are used to generate different permutations of the fire index with respect to the identified variables. Extensive analysis of the results proves the efficacy of the proposed scheme with a significantly reduced error rate of 2.01% for humidity and an error rate of 1.94% for temperature.