Data Reduction Approach Based on Fog Computing in IoT Environment

This paper investigates a data processing model for a real experimental environment in which data is collected from several IoT devices on an edge server where a clustering-based data reduction model is implemented. Then, only representative data is transmitted to a cloud-hosted service instead of raw data. In our model, the subtractive clustering algorithm is employed for the first time for streamed IoT data with high efficiency. Developed services show the real impact of data reduction technique at the fog node on enhancing overall system performance. High accuracy and reduction rate have been obtained through visualizing data before and after reduction

IoT Based Compressive Sensing for ECG Monitoring

The Internet of Things (IoT) has empowered several sets of applications related to remote monitoring for patients with chronic cardiovascular diseases, where, electrocardiogram (ECG) monitoring has been widely studied and applied. Furthermore, in order to optimize the energy consumption in these monitoring systems, compression techniques have been widely deployed. Compressive sensing (CS) has gained a lot of attention in ECG monitoring as a result of its ability to leverage the ECG signal structure in order to achieve a high efficient acquisition scheme. The paper investigates the incorporation of CS in IoT-based ECG monitoring platforms. The platform consists of a CS-based compression and recovery, in addition, the platform provides an abnormality detection for each heart beat using different pattern recognition algorithms. The obtained results reveal that transmitting only 15 % of the samples is enough to recover the signal efficiently. Moreover, using up to 20% of the total sample can achieve a high classification accuracy as using the original data with a maximum drop down of 3.3 % in the worst case scenario. 2017 IEEE.This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors