Fuzzy Based Clustering Algorithms for Enhancing Qos Prediction on The Internet of Things

Abstract

Mobile applications have been a frequently applied method of offering configurable tools for the prevailing implementation of Internet-of-Things (IoT) in modern society. Given that the majority of providers are high and is also growing exponentially, it is an essential objective to evaluate a user's availability for a customer experience. Two basic activities are expected that are proposals for the product and availability of the system. The quality - of - service (QoS) estimation is an essential approach to achieve the different processes, and many approaches were developed for forecasting QoS values. Nevertheless, few approaches are used in IoT systems to test QoS estimation, where qualitative knowledge is crucial. In this paper, it establishes a comprehensive architecture for targeting the IoT system QoS estimation that focuses on Collaborative filtering (CF) and Fuzzy clustering. This paper develops a fuzzy clustering technique efficient of clustering contextual knowledge and then suggest a new method of computing similarities in combination. Next is the development of a modern CF platform that might exploit local and global functionality. Sufficient studies are carried out on two real-world samples, as well as the test data validate the feasibility of the system suggested.