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by Chan Cheung-wing. / Bibliography: leaves 62-63 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1986

The number of devices connected to the Internet will increase exponentially by 2020, which is smoothly migrating the Internet from an Internet of people towards an Internet of Things (IoT). These devices can communicate with each other and exchange information forming a wide Wireless Sensor Network (WSN). WSNs are composed mainly of a large number of small devices that run on batteries, which makes the energy limited. Therefore, it is essential to use an energy efficient routing protocol for WSNs that are scalable and robust in terms of energy consumption and lifetime. Using routing protocols that are based on clustering can be used to solve energy problems. Cluster-based routing protocols provide an efficient approach to reduce the energy consumption of sensor nodes and maximize the network lifetime of WSNs. In this thesis, a single hop cluster-based network layer routing protocol, referred to as HRHP, is designed. It applies centralized and deterministic approaches for the selection of cluster heads, in relation to offer an improved network lifetime for large-scaled and dense WSN deployments. The deterministic approach for selecting CHs is based on the positive selection mechanism in the human thymus cells (T-cells). HRHP was tested over six different scenarios with BS position outer the sensing area, it achieved a maximum average of 78% in terms of life time. To further reduce energy consumption in WSN, a multi-hop algorithm, referred to as MLHP, is proposed for prolonging the lifetime of WSN. In this algorithm, the sensing area is divided into three levels to reduce the communication cost by reducing the transmission distances for both inter-cluster and intra-cluster communication. MLHP was tested over fourteen cases with different heterogeneity factors and area sizes and achieved a maximum of 80% improvement in terms of life time. Finally, a real-time and autonomous emergency evacuation approach is proposed, referred to as ARTC-WSN, which integrates cloud computing with WSN in order to improve evacuation accuracy and efficiency for smart buildings. The approach is designed to perform localized, autonomous navigation by calculating the best evacuation paths in a distributed manner using two types of sensor nodes (SNs), a sensing node and a decision node. ARTC-WSN was tested in five scenarios with different hazard intensity, occupation ratio and exit availability over three different areas of evacuation and achieved an average of 98% survival ratio for different cases.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

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Thesis (M.A.) - Simon Fraser University, 2005. / Theses (School of Communication) / Simon Fraser University. Also issued in digital format and available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 90-102).