Probabilistic QoS Analysis in Wireless Sensor Networks

Authors

Yunbo Wang, University of Nebraska-LincolnFollow

Date of this Version

Spring 5-2012

Document Type

Article

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy, Major: Computer Science, Under the Supervision of Professors Steve Goddard and Mehmet Can Vuran. Lincoln, Nebraska: April, 2012

Copyright (c) 2012 Yunbo Wang

Abstract

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Traditional analysis work only focuses on the first-order statistics, such as the mean and the variance of the QoS performance. However, due to unique characteristics of WSNs, a cross-layer probabilistic analysis of QoS performance is essential. In this dissertation, a comprehensive cross-layer probabilistic analysis framework is developed to investigate the probabilistic evaluation and optimization of QoS performance provided by WSNs. In this framework, the distributions of QoS performance metrics are derived, which are natural tools to discover the probabilities to achieve given QoS requirements. Compared to first-order statistics, the distribution of these metrics reveals the relationship between the performance of QoS-based operations and the probability to achieve the performance. Using a Discrete-Time Markov queueing model in node-level analysis and fluid models in network-level analysis, the distributions of end-to-end delay, the network lifetime, and the event detection delay are then analyzed. Based on the evaluation of QoS metrics, a probabilistic optimization framework is developed to demonstrate the investigation of the optimal network and protocol parameters. Guidelines of designing networks and choosing optimal parameters for WSNs are provided using the optimization framework. Intensive testbed experiments and simulations are used to validate the accuracy of the proposed evaluation and optimization framework.

Advisers: Steve Goddard and Mehmet Can Vuran