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

Abstract

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 efficient. 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.

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

“…The Data Pre‐processing Module filters and converts data into required formats and forwards to the appointed worker node which might be in Edge or Cloud. Deep Learning Models: These are saved in a shared repository and act as the on‐the‐fly learning models that adapt to the performance metrics or loss functions defined by developers to account for the user requirements and application performance. The ease of availability of the model comes with challenges like attacks from hackers that might steal the model or maliciously change it to bring down the accuracy or performance of the predictions of the model . Another challenge is how to maintain the training data private and yet allow all users to access the deep learning models and utilize them without complex authentication mechanisms.…”

“…Other works focus on developing novel algorithms to provide healthcare services with minimum Service Level Agreement (SLA) violations and reducing response time of results . Many research works also design new techniques to increase the security and reliability of such systems like . Further, architecture‐level optimizations have been proposed in Wireless Body Sensor Nodes (WBSNs) that exploit emerging technologies, in order to reduce energy consumption and enhance performance …”

“…Moreover, large computation power is costly, which makes the affordability of healthcare for all people the second biggest challenge . The third challenge is maintaining integrity and reliability of systems . The healthcare data is very sensitive since it can be maliciously used to target a specific sector of people by terrorists or pharmaceutical monopolists.…”

“…As discussed in Section 1, the challenges of providing healthcare to all are difficult to solve. There have been many efforts in recent years to mitigate the problems and providing more efficient systems . However, most of such systems focus on a specific problem and do not deliver a holistic framework that solves the larger issue of providing high performance, affordable, secure, robust, scalable and efficient approach for healthcare applications.…”

“…9 The third challenge is maintaining integrity and reliability of systems. 10 The healthcare data is very sensitive since it can be maliciously used to target a specific sector of people by terrorists or pharmaceutical monopolists. This requires such frameworks to be tamper-proof and hack free, preventing any fraudulent manipulation of sensitive healthcare data.…”