Self-adaptation modeling for service evolution on the Internet of Things (IoT)

Abstract

One of the most important concepts of the internet of things is related to software services, where the system must be able to provide real-time data collected from various environments. So that software services represent a variety of physical and virtual real-world objects that can grow very fast. This condition is related to the ability of self-adaptation software services. However, the existing model of self-adaptation is generally not paying attention to the requirements for service evolution. The objective of this paper is to introduce self-adaptation modeling techniques which consist of, first, domain modeling of the internet of things to represent real-world context; second, developing an inference engine for context inference. As a form of evaluation, this model is applied to the patient monitoring system that will relate to the concept of self-adaptation of various systems, devices, actors, and environment. The case study results show the system's ability to anticipate changes in context and growth needs.