FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing

By Shreshth Tuli, Md. Redowan Mahmud, Shikhar Tuli, Rajkumar Buyya. Journal of Systems and Software, 2018

352 citations recorded by the source corpus.

Abstract Recently much emphasize is given on integrating Edge, Fog and Cloud infrastructures to support the execution of various latency sensitive and computing intensive Internet of Things (IoT) applications. Although different real-world frameworks attempt to assist such integration, they have limitations in respect of platform independence, security, resource management and multi-application execution. To address these limitations, we propose a framework, named FogBus that facilitates end-to-end IoT-Fog(Edge)-Cloud integration. FogBus offers platform independent interfaces to IoT applications and computing instances for execution and interaction. It not only assists developers to build applications but also helps users to run multiple applications at a time and service providers to manage their resources. Moreover, FogBus applies Blockchain, authentication and encryption techniques to secure operations on sensitive data. Due to its simplified and cross platform software systems, it is easy to deploy, scalable and cost efficient. We demonstrate the effectiveness of FogBus by creating a computing environment with it that integrates finger pulse oximeters as IoT devices with Smartphone-based gateway and Raspberry Pi-based Fog nodes for Sleep Apnea analysis. We also evaluate the characteristics of FogBus in respect of other existing frameworks and the impact of various FogBus settings on system parameters through deployment of a real-world IoT application. The experimental results show that FogBus is comparatively lightweight and responsive, and different FogBus settings can tune the computing environment as per the situation demands.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/164e7be27409eff36ad4a8f2ebb355fa3bde3540

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shreshth Tuli
2. Sukhpal Singh Gill
3. Shikhar Tuli

In Computer Vision and Pattern Recognition

1. Shreshth Tuli
2. Sukhpal Singh Gill

Citations in the corpus

• Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing (2020, positive): “We used the HealthFog [11] framework leveraging the FogBus [13] for deploying multiple analysis tasks in an ensemble learning fashion to predict various metrics, like the number of anticipated facilities to manage patients and the hospitals.”