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IEEE Final Year Project Topics for CSE

Base Paper Title

Context-Aware Service Provisioning via Agentized and reconfigurable multimodel cooperation for real life IOT

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IEEE Project Abstract

For the upcoming Internet of Things (IoT) enabled era, context-aware service provisioning (CaSP) can be realized by first analyzing new input data, followed by inferring contexts from the input data, and providing new services based on the inferred contexts. Over time, further new contextual models will also be incorporated into CaSP due to growing data collected by existing or new IoT devices. Furthermore, these contextual models require ongoing adaptation because a real-life environment is dynamic in nature. It becomes more challenging to maintain and adapt these ever-increasing contextual models as the system evolves. To address these concerns, this paper proposes a CaSP infrastructure along with an agentized and reconfigurable design to improve system adaptability and extensibility. The proposed middleware-enhanced CaSP infrastructure can keep as much previously learned knowledge as possible to share among all integrated components. This design reduces the overhead from integrating and adapting multiple contextual models in response to inevitable uncertainties in a dynamically changing IoT-enabled smart home environment. Our agentized design generalizes the scheme of all smart components integrated with the CaSP infrastructure, thus facilitating reciprocal cooperation among all initially independent components. The CaSP infrastructure can facilitate multilevel rather than single-level information reuse via message queues residing in the middleware. This reconfigurable design enables all of the smart components to become loosely coupled and flexibly interconnect to form reconfigurable agents. Such a design allows temporal reconfiguration by reusing as many existing or even new features and contexts on the CaSP infrastructure, thus improving extensibility. Our experimental results show that the proposed CaSP infrastructure can improve the overall adaptability (by about 20%) and extensibility (by 44% to 95%) of CaSP in a dynamic environment.

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