By: Zhen Guo
Advisor: Mengchu Zhou
Department of Electrical and Computer Engineering

Time: 3:00 PM, Tuesday, December 13th, 2005.
Place: Room 202, ECE Center, New Jersey Institute of Technology, Newark NJ. Directions

Abstract

The emerging wireless technologies have made ubiquitous wireless access a reality and enabled wireless systems to support a large variety of applications. Since the wireless self-configuring networks do not require infrastructure and promise greater flexibility and better coverage, wireless ad hoc and sensor networks have been under intensive research.

Dynamics of the object distribution is one of the most important features of the wireless ad hoc and sensor networks. This dissertation deals with several interesting estimation and optimization problems on the dynamical features of ad hoc and sensor networks. Many demands in the applications, such as reliability, power efficiency and sensor deployment, of wireless ad hoc and sensor networks can be improved by mobility estimation and/or prediction. In this dissertation, we study several random mobility models, and present a mobility prediction methodology that relies on the analysis of the moving patterns of the mobile objects. Through estimating the future movement of objects and analyzing the tradeoff between the estimation cost and the quality of reliability, this dissertation work discusses the optimization of tracking interval for sensor networks. Based on the observation on the location and movement of objects, it proposes an optimal sensor placement algorithm by adaptively learn the dynamical object distribution. Moreover, it estimates the dynamical boundary of mass objects monitored in a sensor network based on the unsupervised learning of the distribution density of objects.

In order to provide an accurate estimation of mobile objects, this dissertation work first studies several popular mobility models. Based on these models, we present some mobility prediction algorithms accordingly, which are capable of predicting the moving trajectory of objects in the future. In wireless self-configuring networks, an accurate estimation algorithm allows for improving the link reliability, power efficiency, reducing the traffic delay and optimizing the sensor deployment. The effects of estimation accuracy on the reliability and the power consumption have been studied and analyzed. A new methodology is proposed to optimize the reliability and power efficiency by balancing the trade-off between the quality of performance and estimation cost. By estimating and predicting the mass objects’ location and movement, the proposed sensor placement algorithm demonstrates a significant improvement on the detection of mass objects with near-maximal detection accuracy. Quantitative analysis on the effects of mobility estimation and prediction on the accuracy of detection by sensor networks can be conducted with recursive Expectation-Maximization algorithms. Future research includes the deployment of the proposed concepts and algorithms into real-world ad hoc and sensor networks.

Committee Members:
Mengchu Zhou, Advisor, Professor, ECE Dept., NJIT
Frank Shih, Professor, CS Dept., NJIT
Hongya Ge, Associate Professor, ECE Dept., NJIT
Swades De, Assistant Professor, ECE Dept., NJIT
Jie Hu, Assistant Professor, ECE Dept., NJIT