Advanced Route Planning for Effective Data Acquisition in Distributed Sensor Networks for Environmental Monitoring

Large-scale ecological monitoring is deployed for biodiversity conservation, habitat and water-quality assessment, wildfire detection, and landscape restoration. Although wireless sensor networks provide continuous in-situ measurements across heterogeneous terrains, planning efficient service and mobile data-collection routes to numerous spatially dispersed nodes remains challenging in expansive, access-limited regions. This study introduces route-planning algorithms that minimize total path length while guaranteeing coverage of each node’s operational neighborhood. The framework integrates heuristics for the Traveling Salesman Problem with Neighborhoods with geometric optimization of visitation points; specifically, a centroid-based strategy groups the nodes and computes a minimum enclosing circle for each group. The algorithms are implemented as modular components for route construction, visitation-point adjustment, and coverage evaluation. Performance is validated through simulations over wireless sensor networks with varying densities and spatial layouts, using route length, maneuver count, and computation time as metrics. Relative to baseline methods, the method yields 6–9% shorter routes together with improved coverage in large-scale data-collection scenarios. These results support the design of energy-efficient ecological monitoring systems that leverage intelligent route planning and adapt to diverse terrains and deployment constraints. Implementation details and code modules are provided for integration.

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