Inference Model for Self-Adaptive IoT Service Systems

Aradea, Aradea and Rianto, Rianto and Mubarok, Husni (2021) Inference Model for Self-Adaptive IoT Service Systems. International Journal Of Intelligent Engineering & Systems.

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Abstract

Abstract: Internet of Things (hereafter, IoT) service is a complex system because it should meet miscellaneous domain forms represented physically and virtually. The main challenge of IoT is to provide an inference model to resolve the dynamic context on a run-time basis. The system should have the ability to catch instances or concrete IoT services. On the other side, it should have the capability to adapt to the newest evidence of contexts. This paper introduces an inference model consisting of an IoT structure service artifact, a subsystem of contextual knowledge, and a subsystem of run-time adaptability reasoning. The results of model implementation on monitoring system of coronavirus disease revealed that the ability to adapt continuously and provide various alternative solutions to handle uncertain contexts, which is refered to sensor, network and server failure. The example of experiment result when a sensor failure occurs, the data received by the main server from the node is the average of the three previous data. Keywords: Autonomic computing, Inference model, Self-adaptive systems, IoT service, Service knowledge.

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