Clustering‐based resource discovery on Internet‐of‐Things

Resource discovery on Internet‐of‐Things paradigm is an eminent challenge due to data‐specific activities with respect to foraging and sense‐making loops. The prerequisite to deal with the challenge is to process and analyze the data that require resources to be indexed, ranked, and stored in an efficient manner. A novel clustering technique is proposed to resolve the specified challenge. The technique, namely, iterative k‐means clustering algorithm, targets concrete cluster formation using similarity coefficients of vector space model and performs efficient search against matching criteria with respect to complexity. It is simulated on MATLAB, and the obtained results are compared with fuzzy k‐means and fuzzy c‐means clustering algorithm with similarity coefficients of vector space model against exponential increase in the number of resources.