FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
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Abstract (arXiv)
The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog and Cloud infrastructure. Although there are a number of real-world frameworks attempt to support such integration, they have many limitations from various perspectives including platform independence, security, resource management and multi-application assistance. To address these limitations, we propose a simplified but effective framework, named FogBus for facilitating end-to-end IoT-Fog(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 applications but also helps 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. Because of its lightweight and cross platform software systems, it is easy to deploy, scalable and cost e_cient. We demonstrate the effectiveness of our framework by creating a computing environment with it that integrates finger pulse oximeter as IoT devices with Smartphone-based gateway and Raspberry Pi-based Fog nodes for Sleep Apnea analysis. We also run several experiments on this computing environment varying FogBus settings. The experimental results show that different FogBus settings can improve latency, energy, network and CPU usage of the computing infrastructure.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Nov 2018.
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Citing papers per year: 2021: 4; 2022: 3.
7 papers.
- 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environmentscited by 11
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computingcited by 8
- 2021GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environmentscited by 5
- 2021MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systemscited by 4
- 2022CAROL: Confidence-Aware Resilience Model for Edge Federationscited by 3
- 2021STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Managementcited by 0
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environmentscited by 0
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments21 May 2022
- 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments21 May 2022
- 2022CAROL: Confidence-Aware Resilience Model for Edge Federations14 Mar 2022
- 2021GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments16 Dec 2021
- 2021MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems14 Dec 2021
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing4 Dec 2021
- 2021STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management4 Apr 2021