Semisupervised Classification Based Clustering Approach in WSN for Forest Fire Detection

Forest fire is a very considerable problem of ecological system. This paper depicts a novel technique which detects the high active(HA) zone (nearer to the epicenter of fire) in the forest and transmits all sensed data to the base station through wireless communication as early as possible. Fire office takes necessary action to prevent the spreading of fire. For this purpose sensors are deployed in forest zone to sense different data which are necessary for detecting forest fire and divides it into different clusters. A semisupervised rule-based classification model is proposed in this paper to detect whether its zone is high active, medium active (MA) or low active (LA) cluster in the forest. We train our proposed integrated model in such a way when only one parameter of sensed data is transmitted by the sensor nodes due to energy constraint to the initiator of that zone, initiator can be able to predict the state of (HA,MA,LA) zone with 96% accuracy. All the sensor nodes in HA cluster transmit their packet through cluster head to the base station continuously applying greedy forwarding technique. Authors consider energy saving strategy during cluster head selection and data transmission in HA zone. On the other hand, sensors in MA zone transmit packet periodically and LA zone avoids to transmit the sensed data. This way proposed technique transmits the sensed data from HA zone efficiently and quickly to forest office for forest fire prevention and saves the energy of all sensor nodes in the forest.