An efficient algorithm for partially matched services in internet of services

Abstract

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device is encapsulated as a real-world service that provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, the internet of services is highly dynamic and continuously changing due to constant degradation, vanish and possibly reappearance of the devices, this opens a new challenge in the process of resource discovery and selection. In response to the increasing numbers of services in the discovery and selection process, there is a corresponding increase in the number of service consumers and the consequent diversity of quality of service (QoS) available. An increase in both sides’ leads to diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.

Bo Yuan

Lecturer in (Assistant Professor) Computer Science

My research interests include Data Science, Artificial Intelligence, Machine Learning, Internet of Things, Distributed Computing, Edge Computing