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17 February 2016: One of the CoReNeT member Ms. Sadaf Yasmin has successfully defended her PhD (CS) Thesis titled, "Cost-Effective Routing and Cooperative Framework for Opportunistic Routing", held at Capital University of Science and Technology (CUST)  
In Opportunistic Networks most of Internet's basic assumptions do not hold true. Due to sparse density of nodes and frequent changes in network topology, an end-to-end contemporaneous path may not exist. However, sporadic links emerging from coarse-grained mobility of nodes can be construed over a period of time, as presence of a complete path between a pair of nodes. Nodes hold a packet in permanent storage until an appropriate communication opportunity arises, which can help in further forwarding of the packet. In order to avoid packet loss, multiple copies of a single message are generally sent within the network, independently making their way to eventual destination. This design decision poses extra burden over network resources, and unnecessary utilization may result in degrading performance in resource-stringent environments. Hence, there is need to reduce this extra overhead, by determining effective next-hop utility of nodes, and to better utilize network capacity with real time comprehension of dynamic network characteristic. Heterogeneity of nodes, in terms of capabilities or mobility patterns poses several challenges in defining a utility function that fits all. Moreover, multi-hop routing protocols generally assume altruistic behavior of nodes. However, this assumption is not always true, as by agreeing to forward messages a node is contributing its resources such as memory, processing power, energy etc. Non-cooperative behavior may reduce effective node density and can be devastating in opportunistic environments, where intermediary hops are required to share custody of messages. We target these issues in this thesis. In order to address first problem, we present a ``Multi-Attribute Routing Scheme'' (MARS) based on ``Simple Multi-Attribute Rating Technique'' (SMART) that collects samples of important information about a node's different characteristics. This stochastic picture of a node behavior is then effectively employed in calculating its next-hop fitness. We also devise a method based on learning rules of neural networks to dynamically determine relative importance of each dimension. Hence, estimations based on an optimized combination of multiple parameters help in taking wiser decisions in relay nodes selection with inherent advantage of efficient utilization of network capacity.
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