Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance
Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing. However, this problem is computationally challenging to solve, especially for massive sewage networks having complicated topologies. In this paper, we formulate this problem as a multi-objective optimization problem to consider the conflicting objectives and put forward a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks. The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong. The experiments on small-scale synthetic networks demonstrate a consistent efficiency improvement with reasonable optimization performance and the real-world application shows that our method is effective in generating optimal sensor placement plans to guide policy-making.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Optimal PMU Placement for Kalman Filtering of DAE Power System Models
Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power …
BenchmarkingState EstimationRobust Subset Selection by Greedy and Evolutionary Pareto Optimization
Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however,…
Optimal Coupled Sensor Placement and Path-Planning in Unknown Time-Varying Environments
We address path-planning for a mobile agent to navigate in an unknown environment with minimum exposure to a spatially and temporally varying threat field. The threat field is estimated using pointwise noisy measurements…
Computational EfficiencyNavigateMulti-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints
We develop greedy algorithms to approximate the optimal solution to the multi-fidelity sensor selection problem, which is a cost constrained optimization problem prescribing the placement and number of cheap (low signal-…
Optimizing Sensor Network Design for Multiple Coverage
Sensor placement optimization methods have been studied extensively. They can be applied to a wide range of applications, including surveillance of known environments, optimal locations for 5G towers, and placement of mi…
Deep Learning