The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.

分布式无线传感器网络（WSN）中的检测和估计领域有多种应用，包括军事监视、可持续性、健康监测和物联网（IoT）。与有线集中式传感器网络相比，分布式无线传感器网络具有可扩展性和对传感器节点故障的鲁棒性等优点。在这本书中，我们讨论了估计分布式无线传感器网络结构的问题。首先，我们提供了一个文献综述：（a）图论；（b）网络面积估计；（c）现有的一致性算法，包括平均一致性和最大一致性。其次，介绍了一种在无线传感器网络中计算节点总数的分布式算法。然后介绍了一种分布式网络度分布估计（DNDD）算法。DNDD算法基于平均一致性和网络内经验质量函数估计。最后，给出了一种全分布式的无线传感器网络中心和覆盖区域估计算法。所介绍的算法适用于大多数连接的分布式网络。对算法的性能进行了理论分析，并进行了仿真验证。在这本书中，我们还描述了如何使用引入的算法来学习全局数据信息和全局数据区域。