Sensors: Models and Algorithms for Efficient Design and Operation of Wireless Sensor Networks

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

This grant provides funding for an investigation of effective and efficient deployment (strategic), topology discovery (tactical), and data routing (operational) decisions in wireless sensor networks. These decisions will be considered in an integrated fashion and special emphasis will be given on three desirable attributes regarding sensor networks simultaneously. These attributes are energy efficiency, fault tolerance, and exploitation of communication versus computation trade-offs. Integer and nonlinear mathematical models that aim to capture these characteristics simultaneously and minimize the energy consumption and/or maximize effective sensor network lifespan will be developed. These models will provide sufficient flexibility to be used for making the above strategic-tactical-operational decisions both simultaneously and/or individually. On the methodological side, efficient solution algorithms that provide high quality solutions in reasonable time frames will be developed. These algorithms will include approaches that utilize the specificities of sensor network structures and operations in an effective way. If successful, the results of this grant will introduce new avenues of research in the theory of wireless sensor networks and provide novel analytical models and solution methodologies for important practical problems in this developing area. The results will provide desirable design and operation characteristics for sensor networks so that the benefits from these rather unusually constrained networks are maximized. In addition, a network-planning framework that underlines the interrelationships among different decision levels under the umbrella of unique sensor network attributes will be developed. This framework will help us understand the dynamics involved in making decisions where time frames for different level decisions (strategic-tactical-operational) are not as different as in the traditional contexts. The proposed work will also contribute to the computational tools and methodologies available for integer and nonlinear optimization problems.