Scalability and Satisfiability of Quality-of-Information in Wireless Networks - 2018


Quality of knowledge (QoI) provides a context-dependent live of the utility that a network delivers to its users by incorporating non-ancient info attributes. Quickly and easily predicting performance and limitations of a network using QoI metrics may be a valuable tool for network style. Even additional useful is an understanding of how network parts like topology, bandwidth, and protocols, impact these limitations. In this Project, we have a tendency to develop a QoI-based framework that can offer correct estimates for limitations on network size and achievable QoI necessities, focusing on completeness and timeliness. We have a tendency to extend this framework to model competing flows and knowledge loads as random variables to capture the stochastic nature of real networks. We tend to show that our framework can provide a characterization of delays for happy queries to further analyze performance when some late arrivals are acceptable. Analysis shows that the big tradeoffs exist between network parameters, like QoI requirements, topology, and network size. Simulation results additionally offer evidence that the developed framework can estimate network limits and delays with high accuracy. Finally, this Project conjointly introduces scalably possible QoI regions, that offer upper bounds on QoI necessities that may be supported for sure network applications.

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