Analyzing ECG waves in Fog Computing Environment using Raspberry Pi Cluster
Pratik Kanani, Mamta Padole · 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · 2020
Internet of Things (IoT) and Cloud computing technologies are together serving different Health Care applications. These applications suffer from slower decision-making due to network delays, less availability of bandwidth, transmission delays, processing delays, and data de-noising. To avoid this, Fog computing can be applied as a middle layer between the IoT and Cloud layers. Here, to understand the Fog computing and its characteristics applications along with its advantages and disadvantages are analyzed. Fog computing greatly reduces transmission delays, but Fog devices lag in computing capabilities due to their limited processing power. This can be solved by deploying multiple devices and synchronizing their computation to enable parallel execution. In this paper, a single Raspberry Pi is used as a Fog node and multiple such Raspberry Pis are deployed in the cluster form. The Dispy Python framework is used to allow parallel processing. Scalability is easy to achieve with Dispy, and also it provides different features like automated node discovery, job distribution, processing function distribution, remote access, and enhanced security. The system is proposed and implemented to test the hypothesis that it improves the real-time computation in health care applications. The final results are compared with a traditional processing system and it is found that the Raspberry PI cluster, and Dispy can enhance the computation performance in Health Care.