A distributed cooperative algorithm for localization in wireless sensor networks using Gaussian mixture modeling
Tashnim Jabir Shovon Chowdhury · OhioLink ETD Center (Ohio Library and Information Network) · 2016
Wireless sensor networks are dened as spatially distributed autonomous sensors to monitor certain physical or environmental conditions like temperature, pressure, sound, etc. and incorporate the collected data to pass to a central location through a network.Multifarious applications including cyber-physical systems, military, eHealth, environmental monitoring, weather forecasting, etc. make localization a crucial part of wireless sensor networks.Since accuracy and low computational time of the localization, in case of some applications like emergency police or medical services, is very important, the main objective of any localization algorithm should be to attain more accurate and less time consuming scheme.This thesis presents a cooperative sensor network localization scheme that approximates measurement error statistics by Gaussian mixture.Expectation Maximization (EM) algorithm has been implemented to approximate maximum-likelihood estimator of the unknown sensor positions and Gaussian mixture model (GMM) parameters.To estimate the sensor positions we have adopted several algorithms including Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton (QN), Davidon-Fletcher-Powell (DFP), and Cooperative Least Square (LS) algorithm.The distributive form of the algorithms meet the scalability requirements of sparse sensor networks.The algorithms have been analyzed for dierent number of network sizes.Cramer Rao iii First and foremost, I would to express my sheer gratitude and appreciation for my advisors, Dr.