Context-Aware Offloading for IoT Application using Fog-Cloud Computing

Abstract

It is difficult to run delay-sensitive applications and the cloud simultaneously due to performance metrics such as latency, energy consumption, bandwidth, and response time exceeding threshold levels. This is the case even though advanced networks and technologies are being used. The middleware layer of the Internet of Things (IoT) architecture appears to be a promising solution that could be used to deal with these issues while still meeting the need for high task offloading criterion. The research that is being proposed recommends implementing Fog Computing (FC) as smart gateway in middleware so that it can provide services the edge of the networks. Applications that are sensitive to delays would then be able to be provided in an efficient manner as a result of this. A smart gateway is proposed as solution for taking the offloading decision based on the context of data, which offers a hybrid approach in order to make decisions regarding offloading that are efficient and effective. A solution that uses machine-learning reasoning techniques to make offloading decisions, in multiple fog-based cloud environments. Feature selection, and classification are used as a learning method and are also ensembled as hybrid logistic regression-based learning to provide the best offloading solution. It works by learning the contextual information of data and identify the cases to make the decision of offloading. The proposed model offers a solution that is both energy and time efficient, with an overall accuracy of approximately 80 percent. With the proposed intelligent offloading approach, it is expected that Internet of Things applications will be able to meet the requirement for low response time and other performance characteristics.