Distributed Network Structure Estimation Using Consensus Methods

Description

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. Preface Acknowledgments Introduction Wireless Sensor Networks Applications Consensus Methods in Distributed WSNs Network Structure Estimation Organization of the Book Review of Consensus and Network Structure Estimation Graph Representation of Distributed WSNs Review of Consensus Algorithms Average Consensus Max Consensus Review of Network Structure Estimation Network Connectivity State Estimation System Size Estimation Network Coverage Region Estimation Distributed Node Counting in WSNs System Model Distributed Node Counting Based on L_2 Norm Estimation Phase I: L_2 Norm Estimation Phase II: L_2 Norm Consensus Phase III: Node Counting Performance Analysis Simulation Results Noncentralized Estimation of Degree Distribution System Model Consensus-Based Degree Distribution Estimation Step I: Generate Initial Values Step II: Average Consensus Step III: Postprocessing Estimation of Degree Matrix Performance Analysis Simulations Network Center and Coverage Region Estimation System Model Estimation of Network Center and Radius Distributed Center Estimation Distributed Radius Estimation Performance Analysis Simulations Discussion: Global Data Structure Estimation Conclusions Notation Bibliography Authors' Biographies Blank Page