Maximum Likelihood Coordinate Systems for Wireless Sensor Networks: from physical coordinates to topology coordinates

TL;DR

This paper introduces maximum likelihood-based topology mapping methods for wireless sensor networks, improving physical layout accuracy from connectivity data, and proposes distributed algorithms for real-time sensor localization in complex environments.

Contribution

It presents a novel maximum likelihood topology map concept, algorithms for millimetre wave networks, and distributed methods for sensor coordinate calculation, enhancing physical layout accuracy.

Findings

Maximum likelihood topology map improves physical layout representation.

Millimetre wave topology mapping algorithm based on maximum likelihood estimation.

Distributed algorithms enable real-time sensor localization in complex environments.

Abstract

Many WSN protocols require the location coordinates of the sensor nodes, as it is useful to consider the data collected by the sensors in the context of the location from which they were collected. Thus, one of the major challenges in WSNs is to determine the coordinates of sensors while minimizing the hardware cost. To address this, numerous localization algorithms have been proposed in the literature. However, outcomes of these algorithms are affected by noise, fading, and interference. As a result, their levels of accuracy may become unacceptable in complex environments that contain obstacles and reflecting surfaces. The alternative is to use topological maps based only on connectivity information. Since they do not contain information about physical distances, however, they are not faithful representatives of the physical layout. Thus, the primary goal of this research is to discover a topology map that provides more accurate information about physical layouts. In doing so, this research has resulted in four main contributions. First, a novel concept Maximum-Likelihood Topology Map for RF WSNs is presented. This topology map provides a more accurate physical representation, by using the probability of packet reception. The second contribution is Millimetre wave Topology Map calculation, which is a novel topology mapping algorithm based on maximum likelihood estimation for millimetre wave WSNs. The third contribution is a distributed algorithm being proposed to calculate the topology coordinates of sensors by themselves as two algorithms above calculate centrally, which requires time. Since a topology map contains significant non-linear distortions, two WSN applications i.e. target searching and extremum seeking, which use a proposed topology map to localize the sensors and perform its specified task are presented as the final contribution of this dissertation.