Optimal resource selection framework for Internet-of-Things. (September 2020) - Bharti, Monika; Kumar, Rajesh; Saxena, Sharad; Jindal, Himanshu
Highlights: Develop an Optimal Resource Selection Framework on Internet-of-Things (ORSF-IoT). It supports optimal resource selection through the proposed algorithms, namely, Resource Discovery and Improved Firefly Algorithms. Propose an Iterative Agglomerative Fuzzy K-Means (IAFKM) Clustering Algorithm for selection of best resources by indexing and ranking them on the basis of matched resources. The framework provides optimal solution for selecting the parametric value having exceeded limit as the brightest firefly in less time. It helps in providing timely alarms to the system for corrective measurements and enhances system performancesignificantly. Graphical abstract: Abstract: The fundamental requirement for communication and computation across distinct application areas on Internet-of-Things is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities with respect to foraging and sense-making loops face challenges due to interoperability. Hence, interoperability emerges as a major bottleneck for the requirement. Therefore, to eliminate the challenge, the paper has proposed an "Optimal Resource Selection Framework for Internet-of-Things" that deals with the interoperability and ease the resource discovery and selection. The framework facilitates formation of semantic knowledge base as Shared Virtual Composite Ontology for capturing dynamic IoT heterogeneous data. Moreover, it supports optimal resource selection through the proposed algorithms, namely, Resource discovery Algorithm and Improved Firefly Algorithm. Both algorithms target coordination and optimization with Shared Ontology, respectively. The feasibility of the framework is checked against data collected from Sutlej river, Ludhiana, Punjab, India. The proposed framework is evaluated using benchmark functions with respect to metrics such as mean, standard deviation, processing and execution time. The obtained results are compared with the existing Nature-Inspired algorithms to confirm the efficiency of the proposed framework.