Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance

• URL: http://arxiv.org/abs/2409.16770v1
• Date: Wed, 25 Sep 2024 09:27:51 GMT
• Title: Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance
• Authors: Sunyu Wang, Yutong Xia, Huanfa Chen, Xinyi Tong, Yulun Zhou,
• Abstract summary: We propose 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.
• Score: 6.133464220178637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Diffusion Models as Network Optimizers: Explorations and Analysis [71.69869025878856]
  generative diffusion models (GDMs) have emerged as a promising new approach to network optimization. In this study, we first explore the intrinsic characteristics of generative models. We provide a concise theoretical and intuitive demonstration of the advantages of generative models over discriminative network optimization.
  arXiv  Detail & Related papers  (2024-11-01T09:05:47Z)
• Optimizing Sensor Network Design for Multiple Coverage [0.9668407688201359]
  We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks. We also introduce a Deep Learning model to accelerate the algorithm for near real-time computations.
  arXiv  Detail & Related papers  (2024-05-15T05:13:20Z)
• An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning [8.725702964289479]
  We propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA) We incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes.
  arXiv  Detail & Related papers  (2023-10-20T09:35:51Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Efficient and robust Sensor Placement in Complex Environments [1.1421942894219899]
  This paper addresses the problem of efficient and unobstructed surveillance or communication in complex environments. We propose a greedy algorithm to achieve the objective. Deep learning techniques are used to accelerate the evaluation of the objective function.
  arXiv  Detail & Related papers  (2023-09-15T17:10:19Z)
• Adaptive Consensus: A network pruning approach for decentralized optimization [0.5432724320036953]
  We consider network-based decentralized optimization problems, where each node in the network possesses a local function. The objective is to collectively attain a consensus solution that minimizes the sum of all the local functions. We propose an adaptive randomized communication-efficient algorithmic framework that reduces the volume of communication.
  arXiv  Detail & Related papers  (2023-09-06T00:28:10Z)
• Fighting the curse of dimensionality: A machine learning approach to finding global optima [77.34726150561087]
  This paper shows how to find global optima in structural optimization problems. By exploiting certain cost functions we either obtain the global at best or obtain superior results at worst when compared to established optimization procedures.
  arXiv  Detail & Related papers  (2021-10-28T09:50:29Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization [0.0]
  The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions.
  arXiv  Detail & Related papers  (2020-09-02T17:12:46Z)
• MineReduce: an approach based on data mining for problem size reduction [58.720142291102135]
  This paper presents an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a for the heterogeneous fleet vehicle routing problem.
  arXiv  Detail & Related papers  (2020-05-15T08:49:50Z)
• Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning [96.01176486957226]
  Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems.
  arXiv  Detail & Related papers  (2020-01-03T11:01:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.