Shift Reduce Parser Based Malicious Sensor Detection for Predicting Forest Fire in WSNs

Abstract

Nowadays wireless sensor networks (WSNs) has been used in enormous applications for data collection in an unfriendly environment. Forest fire makes vast hazard to the consuming plant of the world. To preserve the forest from fire, sensor nodes monitor the environment temperature. If the temperature is increased and it exceeds the threshold, the sensor sends the notification message to the fire monitoring system. The forest fire is measured by Fire Weather Index. Most of the existing fire monitoring systems only highlight in detection, but not the verification of the sensor. Suppose, if the attacker inserts any malicious sensor, the malicious sensor sends false information or create an additional delay in fire monitoring system. As a result, the more chances for the forest to be destroyed by fire. To solve this problem, Shift Reduce Parser based Malicious Sensor Detection (SRP_MSD) in WSN is proposed. The Bivariate Pascal Triangle method hides the original identity of nodes, data route from the malicious observer and sends confidential information to the Base Station. This method is analyzed and evaluated using network simulator-2. The results show that it is possible to detect malicious sensor nodes and send the reliable information to the forest fire monitoring system.