Research on the Deployment Strategy of Mountain Pond Safety Monitoring Sensor Network Based on Multi-Objective Optimization

This paper focuses on the core goal of “low cost”, integrating optimization algorithm modeling to systematically analyze and solve the optimal deployment strategy of sensors under the constraints of coverage capacity, communication network constraints, and data accuracy constraints. By employing a multi-objective optimization framework, this study aims to balance the trade-offs between minimizing the total deployment cost and maximizing the overall system performance. Specifically, the coverage capacity constraint ensures that the entire monitoring area is adequately covered by the sensors, while the communication network constraint guarantees reliable data transmission among sensors and to the central processing unit. The data accuracy constraint ensures that the collected data meet the required precision standards for effective decision-making. To address these challenges, this paper proposes an innovative sensor deployment strategy for mountain ponds. The strategy incorporates a hybrid optimization approach, combining a layered greedy algorithm with a multi-objective optimization model. The extended maximum coverage model integrates communication connectivity, data accuracy, and device cost into a unified objective function, establishing a triple optimization framework for coverage effectiveness, economy, and reliability. The layered greedy algorithm prioritizes high-risk areas through risk level weighting in the first phase and dynamically eliminates inefficient nodes in the second phase, achieving an approximate optimal solution within polynomial time complexity. This research not only provides a practical and cost-effective solution for sensor network deployment in large-scale monitoring applications but also offers a complete technical path from theoretical modeling to algorithm design for the safety monitoring of mountain ponds. Future work will focus on extending this approach to more complex environments, incorporating real-time adaptive adjustments, and conducting scaled verification in collaboration with the water conservancy department. The proposed framework can also be extended to the monitoring and optimization of other hydraulic facilities such as reservoirs and embankments, providing common technical support for the development of smart water conservancy.