Distributed Network Structure Estimation Using Consensus Methods (synthesis Lectures On Communications)
by Andreas Spanias /
2018 / English / PDF
3 MB Download
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.
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.
