An ontology oriented service framework for social IoT

Abstract

Social IoT (S-IoT) is a robust paradigm linked with big data and IoT for the sharing and interaction of various devices and services to obtain the desired goal. In recent applications, it is a challenging task to maintain the trust between various linked devices for the growth and creation of IoT systems. Therefore, this paper presents a separation architecture based on the Quality of Services by managing the trust between the IoT devices. The proposed model is novel based on the selection of the attribute set against the separated class. The training and classification are done by using the ontology architecture. The data architecture is processed by a k-means algorithm for the generation of the class labels into three different groups. The groups have been labeled using a Fuzzy logic inference engine and a Genetic Algorithm has been used for optimization. The performance measure has been evaluated using multiple multiclass classifiers for accuracy, True Positive Rate, (TPR), and False Positive Rate (FPR).The simulation results elucidate that accuracy of the proposed technique is improved by 10.7%, FPR by 6%, and TPR by 11% in comparison to state of art techniques.