The intention of facilitating simultaneous execution for both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity of integrating Edge, Fog and Cloud infrastructure. There exists a notable number of real-world frameworks for attaining such integration. However, the limitations of existing frameworks in terms of platform independence, security, resource management and multi-application assistance resist the potentiality of integrated environment. Therefore, in this paper, we developed a simplified but effective framework, named FogBus for implementing end-to-end IoTFog( Edge)-Cloud integration. FogBus offers a platform independent interface to IoT applications and computing instances for execution and interaction. It not only assists developers in building up applications but also supports users in running multiple applications at a time and service providers to manage their resources. In addition, FogBus applies Blockchain, authentication and encryption techniques to secure operations on sensitive data. Besides, it is easy to deploy, scalable, energy and cost efficient. To demonstrate the efficacy, we also designed a prototype for Sleep Apnea analysis through FogBus framework. The experimental results of this case study show that different FogBus settings can improve latency, energy, network and CPU usage of the computing infrastructure.
The major contributions of this work are listed as:
- A lightweight and simplified framework named FogBus that integrates IoT enabled systems, Fog and Cloud infrastructure and harness both edge and remote resources according to application requirements.
- Exploration of platform independent application execution and node-to-node interaction overcoming heterogeneity within the integrated environment.
- Design of a Platform-as-a-Service (PaaS) model that assists application developers, users and service providers to pursue individual interests.
- Development of a prototype for Sleep Apnea analysis in integrated IoT-Fog-Cloud environment.
- Implementation of block chain technique to ensure data integrity while transferring confidential data.
- Performance evaluation of FogBus in terms of latency, energy, network and CPU usage.
FogBus has been deployed and tested with applicaitons like:
- EdgeLens - Distributed Deep Learning for Object detection harness edge and cloud resources.
- HealthFog - An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and Fog computing environments
For installing FogBus please refer to the User Manual.
For developing custom policies or protocols please refer to the Developer Manual.
Fog Computing, Edge Computing, Cloud Computing, Internet of Things(IoT), Blockchain.
GPL v2.0
To contribute please raise a merge request. If you find any bugs in the code please raise an issue.
FogBus has been developed by:
- Shreshth Tuli
- Redowan Mahmud
- Shikhar Tuli
- Rajkumar Buyya
in the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, at the Computer Science and Software Engineering Department of [the University of Melbourne]
@article{tuli2019fogbus,
title = {{FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing}},
author={Tuli, Shreshth and Mahmud, Redowan and Tuli, Shikhar and Buyya, Rajkumar},
journal = "Journal of Systems and Software",
volume = "154",
pages = "22--36",
year = "2019",
issn = "0164-1212",
doi = "https://doi.org/10.1016/j.jss.2019.04.050",
publisher={Elsevier},
url = "http://www.sciencedirect.com/science/article/pii/S0164121219300822"}
- Shreshth Tuli, Redowan Mahmud, Shikhar Tuli, and Rajkumar Buyya, FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing. Journal of Systems and Software (JSS), Volume 154, Pages: 22-36, ISSN: 0164-1212, Elsevier Press, Amsterdam, The Netherlands, August 2019.
- Shreshth Tuli, Nipam Basumatary, Sukhpal Singh Gill, Mohsen Kahani, Rajesh Chand Arya, Gurpreet Singh Wander, and Rajkumar Buyya, HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments, Future Generation Computer Systems (FGCS), Volume 104, Pages: 187-200, ISSN: 0167-739X, Elsevier Press, Amsterdam, The Netherlands, March 2020.
- Shreshth Tuli, Nipam Basumatary, and Rajkumar Buyya, EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments, Proceedings of the 4th IEEE International Conference on Information Systems and Computer Networks (ISCON 2019, IEEE Press, USA), Mathura, India, November 21-22, 2019.
