A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services

Abstract

This study proposes a Client-Fog-Cloud (CFC) multilayer data processing and aggregation framework that is designed to promote latency-sensitive applications in an IoT context. The framework is designed to address the current IoT-based challenges: wide distribution, massive uploading, low latency, and real-time interaction. The proposed framework consists of the device gateway, the fog server and the cloud. The device gateway collects data from clients and uploads it to the nearest fog node. Received data will be pre-processed and filtered by the fog server before being transferred to the cloud for further processing or storage. An abduction alert fog-based service was implemented to evaluate the proposed framework. Performance was evaluated by comparing the response time and the delay time of the proposed architecture with the traditional cloud computing architecture. Additionally, the aggregation rate was evaluated by simulating the speed of bike riding as well as the walking speed of young adults and elderly. Results show that comparing with the traditional cloud, our proposal noticeably reduces the average response time and the delay time (i.e., whether the newest data or the historical data are being queried). Results indicate the capability of the proposed framework to reduce the response time by 32% and the data transferred to the cloud by 30%.