A Risk-Aware Spatial Deployment Framework Followed by Hierarchical Network Organization and Energy-Aware Performance Assessment

Forest fires are a growing threat in Portugal and in the Mediterranean region, necessitating the development of early detection systems capable of deploying monitoring resources to the highest priority areas. Conventional strategies for sensor placement in WSNs are often based on coverage, distance, and connectivity criteria, without explicitly incorporating the spatial distribution of fire hazard. This study proposes an integrated methodology for risk-oriented WSN planning, using the ICNF’s official rural fire hazard map as a spatial decision criterion. The cartography is converted into a hazard matrix and used to identify priority areas, on which the maximum coverage problem (MCP) was applied through a greedy heuristic to determine the sensor deployment positions. The resulting network was subsequently organized using K-means, with cluster head (CH) selection, relay-assisted multi-hop routing under a 250 m communication range, sink identification, and inter-cluster communication via a minimum spanning tree (MST), interpreted as an idealized connected graph and also recomputed under the same 250 m link constraint. Two alternative scenarios were also evaluated: a less restrictive hazard threshold (classes 3–5) and a random deployment of the same number of sensing nodes, repeated over 100 independent seeds, and a hazard-blind coverage-optimized control. The methodology is applied to the Serra de São Mamede Natural Park, with an area of 56,061.31 ha, where 2995 priority cells classified as high and very high hazard is identified. The greedy heuristic placed 589 sensors that covered all priority cells, whereas random deployments of the same 589 sensing nodes covered only 45.3 ± 1.5% of them (a hazard-blind optimized deployment covered 61.1%), for a similar estimated energy consumption (349.6 J versus 339.4 J per round), which corresponds to about half the energy per covered priority cell; this gain reflects the combined effect of hazard prioritization and coverage optimization. Connecting the sensors under the 250 m range required 1159–1174 relay nodes, and the energy differences among 28–30 clusters were small (≤1.8%) and are treated as descriptive. Extending the priority set to classes 3–5 required 1054 sensors and, with the cluster count held fixed at k = 29, increased energy consumption by 70.6% (21% with k = 53, i.e., about 20 sensors per cluster). The results indicate that the integration of official hazard mapping, spatial optimization, hierarchical organization, and routing constitutes a reproducible approach for risk-oriented WSN planning, with the potential to support early fire detection systems in spatially heterogeneous territories.