Resource discovery in the Internet of Things integrated with fog computing using Markov learning model

Abstract

Due to high and unpredictable connection delays, privacy gaps, and traffic load of networks connecting cloud computing to end users in many of the Internet of Things (IoT)-based services, some challenges have been created in cloud computing efficiency. Hence, fog computing has been proposed as a solution in order to bring the cloud service closer to the existing things in the ecosystem. Integrating IoT with fog computing is associated with many challenges, including the resource discovery process. In one sense, sensors, devices, and things are the resources in the IoT ecosystem that searching for them regarding the quality of the search and selection can be one of the challenges in the resource discovery process. In the present paper, the hidden Markov chain learning method has been used to cope with this challenge in the IoT ecosystem integrated with the fog computing, to determine the probability of the need for each thing or resource in the near future with the aim of reducing latency and increasing the network use. The simulation in this work has been performed in the Cloudsim platform, and the considered parameters in the proposed method have been compared with TOPSIS, VIKOR and SAW methods.