Deployment and replenishment of sensors in wireless monitoring networks

Recent advances in Micro Electro-Mechanical Systems (MEMS) technology, including MEMS sensors, have allowed small, inexpensive, energy-efficient, and reliable sensors with wireless networking capabilities to become a reality. The continuing development of these technologies has given rise to increased interest in the concept of wireless sensor networks (WSN), which have been the subject of extensive research in recent years. A wireless sensor network is composed of a large number of sensor nodes, each consisting of sensing, data processing, and communication components that 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 (BS).In this thesis, we focus on the problem of deploying and replenishing wireless sensor nodes onto an area such that a given mission lifetime is met subject to constraints on cost, connectivity, coverage, and capacity. 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) in a clustered architecture that extends lifetime beyond lifetime experienced with a uniform deployment (as well as other existing differential techniques); (2) a characterization of the energy consumption in a clustered network and the energy remaining after network failure; (3) a strategy for replenishing nodes consisting of determining the optimal order size and the allocation over the deployment region. The deployment and replenishment strategies are developed through analysis of the design constraints imposed by typical sensor nodes. The result is a set of algorithms that provide differential deployment densities for nodes (clusterhead and nonclusterhead) that maximize network lifetime and minimize wasted energy, and, in cases where a single deployment is not feasible, differential densities for the optimal replenishment order sizes that minimizes deployment costs.M.S., Electrical Engineering -- Drexel University, 200