An efficient grey theory-driven path selection for energy efficiency control in the Internet of Things using fog and cloud computing

Abstract

Due to the big data exchange on the Internet of Things, proper routing and selecting the best routes for fast data transmission improve network performance. There are major challenges, like high delay, when cloud computing is used. Therefore, one solution is to use other schemes, such as fog computing. In fog computing, all data is not sent to the cloud and the fog nodes close to objects are used for data processing. This reduces the network delay. In this paper, we propose an overlapping clustering method called MFCT-IoT to select the best cluster head nodes to guarantee the fast data transfer from objects to fog nodes. The selected cluster head nodes are responsible for sending the collected data to the closest fog nodes in the network edge. Upon receiving the data, the fog nodes process it, and if a response is ready, they respond immediately to the object. Otherwise, they merge and transmit the data to the cloud servers, which are considered as the root node of the proposed hierarchical tree. After processing, the merged data is sent to the object. We compare the proposed scheme with two schemes, including ERGID and EECRP. These schemes are evaluated based on various criteria, including the response time, packet delivery ratio, end-to-end delay, network lifetime, and energy consumption. The results indicate that the proposed method outperforms others in terms of all criteria.