Wireless sensor networks (WSNs) face challenges in achieving robust target coverage and connectivity, particularly when varying priorities for targets are modeled with Q-Coverage and Q-Connectivity constraints. However, existing studies often neglect minimizing the number of nodes under these constraints in 3D environments or focus on sensor-to-sensor connections, which are less suitable for target-oriented networks. This paper bridges these gaps by proposing a novel two-phase heuristic approach. In Phase I, we introduce SPARTA, with two variants (SPARTA-CC and SPARTA-CP), to address Q-Coverage. Phase II employs GEMSTONE, a heuristic algorithm based on a minimum spanning tree, to ensure Q-Connectivity. Our method is evaluated on a real-world 3D dataset and compared against baseline methods. The results demonstrate that our approach significantly reduces the number of nodes while improving running speed. Our proposal can save 13% of the node count while running 2370 times faster than the current state-of-the-art method. These contributions advance the state of the art in WSN design and hold significant implications for efficient and fault-tolerant network deployment in practical scenarios.

中文翻译：

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SPARTA-GEMSTONE：在 [公式省略]-覆盖率和 [公式省略]-连通性约束下在 3D WSN 中高效放置节点的两阶段方法


无线传感器网络 （WSN） 在实现强大的目标覆盖和连接性方面面临挑战，尤其是在使用 Q 覆盖率和 Q 连接性约束对目标的不同优先级进行建模时。然而，现有的研究往往忽视了在 3D 环境中最小化这些约束下的节点数量，或者专注于传感器到传感器的连接，这不太适合面向目标的网络。本文通过提出一种新的两阶段启发式方法来弥合这些差距。在第一阶段，我们引入了 SPARTA，它有两种变体（SPARTA-CC 和 SPARTA-CP），以解决 Q 覆盖率问题。第二阶段采用 GEMSTONE，这是一种基于最小生成树的启发式算法，以确保 Q-Connectivity。我们的方法在真实世界的 3D 数据集上进行评估，并与基线方法进行比较。结果表明，我们的方法在提高运行速度的同时显著减少了节点数量。我们的提案可以节省 13% 的节点数量，同时运行速度比当前最先进的方法快 2370 倍。这些贡献推动了 WSN 设计的最新技术，并对实际场景中的高效和容错网络部署具有重要意义。