Risk-Aware Combinatorial Optimization for Sensor Network Deployment in Forest Fire Detection

Abstract

Introduction: Forest fires represent one of the most devastating natural hazards, causing irreversible ecological damage, economic losses, and threats to human safety. Early detection through sensor networks has emerged as a key preventive strategy. Traditional methods of fire monitoring, such as human patrols, watchtowers, or satellite imagery, often suffer from limitations related to cost, latency, and insufficient spatial coverage. Wireless Sensor Networks (WSNs) have emerged as a promising technology for environmental monitoring, thanks to their low cost, scalability, and ability to provide real-time data collection over large and complex terrains[1].Objectives: The effectiveness of such a system depends strongly on the deployment strategy of sensors. Poorly placed sensors can lead to gaps in coverage, excessive energy consumption, or reduced network lifetime. Moreover, in the context of wildfire prevention, it is not sufficient to only maximize coverage: one must also take into account the spatial distribution of fire risks across the monitored area, as some regions may be significantly more vulnerable than others. This work proposes a mathematical model and a metaheuristic-based approach for the optimal deployment of sensors in forest environments with the objective of minimizing fire risk while ensuring coverage and connectivity[2].Methods: To address these challenges, we formulate the forest fire sensor placement problem as a risk-aware combinatorial optimization model, where the objective is to minimize the number of sensors and communication overhead while maximizing coverage in high-risk zones. To solve this NP-hard problem, we propose a hybrid metaheuristic framework combining a Genetic Algorithm with Steiner tree-based repair operators and local search refinements[3]. The optimization goal is to strike a balance between minimizing the number of deployed sensors and maximizing the weighted coverage of high-risk cells. The formulated problem is NP-hard, closely related to the Set Cover Problem, the Connected Set Cover Problem, and the Facility Location Problem. As such, exact methods become impractical for large-scale instances, justifying the need for metaheuristic or hybrid optimization techniques[4].Results: Extensive simulations demonstrate that our method significantly reduces the number of sensors and communication cost compared to classical heuristics, while improving detection reliability in fire-prone regions. The experiments reveal that NSGA-II consistently provides the best solutions, improving upon the heuristic baseline by 5--10%. GA and D-PSO achieve competitive performance, while SA shows higher variance and tends to get stuck in local optima. NSGA-II not only provides the best solutions but also the most robust and stable performance across runs[5].Conclusions: The proposed framework offers a scalable and adaptive solution for next-generation wildfire early-warning systems. This study highlights the importance of realistic modeling, which accounts not only for technical constraints (detection radius, communication range, sensor budget) but also for environmental and stochastic factors (risk mapping, failures). Overall, NSGA-II emerges as the most effective algorithm for the considered problem, while GA and D-PSO serve as good alternatives when faster results are needed[6].