A Forest Adaptive Sensor Topology enhanced Artificial Bee Colony Algorithm (FAST-ABC) for early detection of wildfire

Forest fires constitute a pressing global crisis, demanding urgent, unified action to mitigate their devastating impacts on resources. The frequency of such mishappenings has increased since last few decades due to numerous reasons. Forest fires have multifaceted impacts, including ecological devastation, climate change exacerbation, human health risks, economic losses, and long-term ecosystem disruption. Current works on forest fire monitoring systems constitute of wireless sensor networks as they are most suitable for such disaster management scenarios. However, the state-of-the-art work emphasizes on distributing the sensors or nodes randomly in forests due to their harsh and inconvenient terrains. Such random placement of nodes results in inefficiency and unreliability of the system. To address these issues, a novel algorithm Forest Adaptive Sensor Topology enhanced Artificial Bee Colony Algorithm (FAST-ABC) has been proposed in this work. This work is presented in two phases, In the first phase, the enhanced ABC algorithm is verified in terms of maximum coverage, compared with other state-of-the-art algorithms. In the second phase, a novel algorithm FAST-ABC is incorporated that provides the optimum topology of sensors in the network based on various novel forest parameters. The proposed algorithm identifies the forest regions for sensor deployment thereby preventing the wastage of resources. It successfully provides the topology with maximum coverage irrespective of the various forest aspects.