Reducing Delay and Packets Loss in IoT-Cloud Based ECG Monitoring by Gaussian Modeling

Abstract

Abstract— Health monitoring based on the internet of things (IoT) and cloud computing is regarded as a hot topic to research. However, such systems often face issues with delay and throughput due to the large amount of data that must be transmitted from sensors to the cloud. One important type of data for health monitoring is Electrocardiogram (ECG) signals, which generate a large amount of data to be transmitted. This research treats this problem by modelling these signals in order to reduce their size using Gaussian approximation. The cloud server is an MQTT broker to which the sensors publish their data via a gateway. The Gaussian parameters are calculated in the gateway, which act as a Fog layer, before published to the broker. The monitoring devices can subscribe to the broker and access the transmitted data. Our experiments were conducted using the MIT–BIH dataset and a real broker (HiveMQ). The results showed that the system was able to significantly reduce delay in transmitting data and prevent loss of information. Without using the Gaussian approximation technique, the system was only able to monitor a limited number of patients (17 for Qos1 and 23 for Qos0) without losing information. However, when using the Gaussian approximation model with five functions, the system was able to monitor many more patients (78 for Qos1 and 100 or more for Qos0) without losing any data.

A Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics

“…Despite HTTP's slower speed than MQTT, this study demonstrates that it has less packet loss [7]. A study on the implementation of IoT on the electrocardiogram (ECG) shows that packet loss in MQTT follows the growing number of patients [8]. Network congestion is termed packet loss at the network level in this study.…”

“…Thus, although there is a small packet loss for both protocols at the network level, there may be a greater packet loss at the application level. This research is essential since packet loss can result in erroneous nutrient values, as was the case with IoT-based ECG [8]. More packet loss will occur if the application is down due to resource overload, which will stop the hydroponic system.…”

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

“…Heart disease is one of the most severe diseases, the late detection of which may threaten human life. Among these diseases is arrhythmia, as these diseases are identified by analyzing the electrocardiogram (ECG), which is one of the most popular methods used to check the electrical activity and rhythm of the heart [1,2]. Based on the analysis of the ECG to extract relevant features, the automatic classification models Zahraa Ch.…”

“…Based on the analysis of the ECG to extract relevant features, the automatic classification models Zahraa Ch. Oleiwi 1,2 of heartbeats are constructed to identify different types of heart diseases, including arrhythmia [3]. This paper proposes a novel model based on machine learning models that aim for the early, accurate detection of heart diseases [4].…”