Context-aware search optimization framework on the internet of things

The resource discovery on IoT paradigm requires to be efficient with respect to modeling, storage, processing, and validation of the gathered data. These requirements face challenges like interoperability, heterogeneity, etc, with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics. It leads resource discovery to emerge as a non-linear constrained-specific problem that need to be linearized for its optimization with reduced complexity. Keeping the perspective, a context-aware search optimization framework on the internet of things is introduced, which targets knowledge presentation through schema, discovery via a multi-modal search algorithm, and its optimization through an Iterative Gradient Descent algorithm. The multi-modal search algorithm through keywords, value or spatial-temporal indices performs resource discovery by finding the suited matches as a search set from a search-space. The search set is further evaluated via the iterative gradient descent algorithm for optimization through the usage of iterative and convergence properties of the gradient descent. The search efficiency is tested using various objective functions and resources on MATLAB and is compared with Newton and Quasi-Newton methods. The obtained results depict the efficiency of the algorithm graphically with reference to the searching time, such as validate the system performance.