Wireless Sensor Networking is envisioned as an economically viable paradigm and a promising technology because of its ability to provide a variety of services, such as intrusion detection, weather monitoring, security, tactical surveillance, and disaster management. The services provided by wireless senor networks (WSNs) are based on collaboration among small energy-constrained sensor nodes. The large deployment of WSNs and the need for energy efficient strategy necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has been proven to provide the required scalability and prolong the network lifetime. Due to the bottle neck phenomena in WSNs, a sensor network loses its connectivity with the base station and the remaining energy resources of the functioning nodes are wasted.
This thesis highlights some of the research done to prolong the network lifetime of wireless sensor networks and proposes a solution to overcome the bottle neck phenomena in cluster-based sensor networks. Transmission tuning algorithm for a cluster-based WSNs is proposed based on our modeling of the extra burden of the sensor nodes that have direct communication with the base station. Under this solution, a wireless sensor network continues to operate with minimum live nodes, hence increase the longevity of the system.
An information theoretic metric is proposed as a cluster head selection criteria for breaking ties among competing clusters, hence as means to decrease node reaffiliation and hence increasing the stability of the clusters, and prolonging the network lifetime. This proposed metric attempts to predict undesired mobility caused by erosion.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/3735 |
| Date | 20 May 2008 |
| Creators | Elaneizi, Muattaz |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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