Ph.D. Dissertation Defense: David J. Dorsey
Thursday, July 24, 2014
10:00 AM-11:30 AM
Title: Robust Deployment and Control of Sensors in Wireless Monitoring Networks
Advisor: Dr. Moshe Kam
Date: Thursday, July 24, 2014
Time: 10:00 a.m.
Location: ECE Conference Room 302, 3rd Floor, Bossone Research Enterprise Center
Abstract
The continuing development of small, inexpensive, energy-efficient sensors with wireless networking capabilities has given rise to increased interest in the concept of wireless sensor networks (WSNs). A WSN is composed of a large number (hundreds, even thousands) of sensor nodes, each consisting of sensing, data processing, and communication components. The sensors are deployed onto a region of interest and form a network to directly sense and report on physical phenomena. The goal of a monitoring wireless sensor network is to gather sensor data from a specified region and relay this information to a designated base station (BSt).
In this study, we focus on deploying and replenishing wireless sensor nodes onto an area such that a given mission lifetime is met subject to constraints on cost, connectivity, and coverage of the area of interest. The major contributions of this work are (1) a technique for differential deployment (meaning that nodes are deployed with different densities depending on their distance from the base station); the resulting clustered architecture extends lifetime beyond network lifetime experienced with a uniform deployment and other existing differential techniques; (2) a characterization of the energy consumption in a clustered network and the energy remaining after network failure, this characterization includes the overhead costs associated with creating hierarchies and retrieving data from all sensors ; (3) a characterization of the effects and costs associated with hop counts in the network; (4) a strategy for replenishing nodes consisting of determining the optimal order size and the allocation over the deployment region. The impact of replenishment is also integrated into the network control model using intervention analysis. The result is a set of algorithms that provide differential deployment densities for nodes (clusterhead and non-clusterhead) that maximize network lifetime and minimize wasted energy. When a single deployment is not feasible, the optimal replenishment strategy that minimizes deployment costs and penalties is calculated.