Towards a socionomic framework for collaborative data routing in wireless sensor network
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NO FULL TEXT AVAILABLE. This thesis contains 3rd party copyright material. ----- A Darwinian perspective on the existence of a relationship between change and evolution in nature has long been argued. Over the last 35,000 years human culture has witnessed an enormous change that is in sharp contrast to our biological evolution. Our cultural change has been governed by the attributes of human behaviour -to react, learn and adapt, leading to the formulation of complex social and economic phenomenon . Such traits have allowed human beings to live in groups, experience, contribute, and share knowledge, leading to the formation and association to our respective societies. However, the autonomous traits in humans also create differences in opinion which in turn moulds our perception, experience and action. As a result of the individual differences, no two societies can be exactly alike and a means of assessing the underlying dynamics is through understanding the economic constraints. After all, a society is driven by its resource constraints that is best gauged by realizing the cost of mining and using those resources. Although, such a socionomic model cannot necessarily be formalized, yet, in software intensive systems it establishes a new paradigm for resource management. This thesis establishes the potential for the application of such a socionomic model in optimizing resource usage and collaboration among sensor nodes in a typical Wireless Sensor Network. The synergy of new technological innovations in the realm of Wireless Sensor Networks has paved the way for their application in many different domains. In dyna mic but sometimes hostile environments, such as, battlefields, roads, tunnels, and hospitals, sensor nodes need to be autonomous to be able to maximize their chances of survival without the need for any external aid . However, spatial differences and environmental factors may sometimes make it impossible for sensor nodes to successfully collaborate and exchange information. The application of a socionomic model in such scenarios allows sensor nodes to be programmed with the intelligence to attempt different avenues of communication, cluster formation and data routing. The goal of this research is to optimize resources usage and thereby maximize the lifetime of the network. Finally, the socionomic framework has been validated against more traditional techniques of clustering and data routing, through several experiments and case studies. Some of the novel concepts of selflearning, knowledge acquisition and collaboration methodologies have also been detailed and evaluated against relevant practices and guidelines.
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