Convenience-based periodic composition of IoT services

Abstract

We propose a novel service mining framework to personalize services in an IoT based smart home. We describe a new technique based on the concept of convenience to discover periodic composite IoT services to suit the smart home occupant’s convenience needs. The key features of convenience is the ability to model the spatio-temporal aspects as occupants move in time and space within the smart home. We propose a novel framework for the transient composition of spatio-temporal IoT service. We design two strategies to prune non-promising compositions. Specifically, a significance model is proposed to prune insignificant composite IoT services. We describe a spatio-temporal proximity technique to prune loosely correlated composite IoT services. A periodic composite IoT service model is proposed to model the regularity of composite IoT services occurring at a certain location in a given time interval. The experimental results on real datasets show the efficiency and effectiveness of our proposed approach.