In the Internet of Things (IoT) era, wireless sensor networks play a critical role in communication systems. One of the most crucial problems in wireless sensor networks is the sensor deployment problem, which attempts to provide a strategy to place the sensors within the surveillance area so that two fundamental criteria of wireless sensor networks, coverage and connectivity, are guaranteed. In this paper, we look to solve the multi-objective deployment problem so that area coverage is maximized and the number of nodes used is minimized. Since Harmony Search is a simple yet suitable algorithm for our work, we propose Harmony Search algorithm along with various enhancement proposals, including heuristic initialization, random sampling of sensor types, weighted fitness evaluation, and using different components in the fitness function, to provide a solution to the problem of sensor deployment in a heterogeneous wireless sensor network where sensors have different sensing ranges. On top of that, the probabilistic sensing model is used to reflect how the sensors work realistically. We also provide the extension of our solution to 3D areas and propose a realistic 3D dataset to evaluate it. The simulation results show that the proposed algorithms solve the area coverage problem more efficiently than previous algorithms. Our best proposal demonstrates significant improvements in coverage ratio by 10.20% and cost saving by 27.65% compared to the best baseline in a large-scale evaluation.

中文翻译：

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实现完美平衡：使用 Harmony Search 进行多目标优化，以最大限度地降低部署成本并最大限度地提高覆盖范围


在物联网 （IoT） 时代，无线传感器网络在通信系统中发挥着关键作用。无线传感器网络中最关键的问题之一是传感器部署问题，它试图提供一种策略，将传感器放置在监控区域内，从而保证无线传感器网络的两个基本标准，即覆盖范围和连接性。在本文中，我们希望解决多目标部署问题，以便最大限度地提高区域覆盖率并最大限度地减少使用的节点数量。由于 Harmony Search 是一种简单但适合我们工作的算法，我们提出了 Harmony Search 算法以及各种增强提案，包括启发式初始化、传感器类型的随机采样、加权适应度评估以及在适应度函数中使用不同的组件，以为传感器具有不同感应距离的异构无线传感器网络中传感器部署问题提供解决方案。最重要的是，概率传感模型用于反映传感器的实际工作方式。我们还将我们的解决方案扩展到 3D 区域，并提出了一个真实的 3D 数据集来评估它。仿真结果表明，所提算法比以前的算法更有效地解决了区域覆盖问题。在大规模评估中，我们的最佳方案表明，与最佳基线相比，覆盖率显著提高了 10.20%，成本节省了 27.65%。