A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks



In this paper, we propose a novel swarm-inspired system for detecting wildfires using sensor networks. There are three-fold concerns of this paper. The first is the speed of information propagation, the second is the accuracy of the information being propagated and the third is the reliability of the system over a long period of time. With these in mind, we develop a probabilistic model for focusing on responses for query requests in an accurate manner, similar to the assignment of probabilities of occurrence in a distributed database. We follow a data-centric approach where the system executes a swarm-inspired routing and aggregation algorithm. For effective management of the system, zones of reachability are formed which denote areas, which help the sensor nodes in local maintenance, and through an interaction of these zones, a globally robust system is obtained. The system generates various kinds of reports and is connected to disaster response stations. We simulate our proposed system in NS-2 for certain parameters, and more importantly in the presence of failures, to conclude that the system performs well for different scenarios and very well when an optimal zone radius is chosen. Copyright © 2008 John Wiley & Sons, Ltd.