معماری سه لایه ای اینترنت اشیا برای تشخیص زودهنگام بیماری های قلبی
Abstract
1- Introduction
2- Related work
3- Proposed framework for IoT-based Health Monitoring System
4- Result and discussion
5- Performnace evaluation
6- Conclusion
References
Abstract
Among the applications enabled by the Internet of Things (IoT), continuous health monitoring system is a particularly important one. Wearable sensor devices used in IoT health monitoring system have been generating an enormous amount of data on a continuous basis. The data generation speed of IoT sensor devices is very high. Hence, the volume of data generated from the IoT-based health monitoring system is also very high. In order to overcome this issue, this paper proposes a scalable three-tier architecture to store and process such huge volume of wearable sensor data. Tier-1 focuses on collection of data from IoT wearable sensor devices. Tier-2 uses Apache HBase for storing the large volume of wearable IoT sensor data in cloud computing. In addition, Tier-3 uses Apache Mahout for developing the logistic regression-based prediction model for heart diseases. Finally, ROC analysis is performed to identify the most significant clinical parameters to get heart disease.
Introduction
In recent years, there has been a perceptible increase the number of wearable devices for monitoring the patients’ health, fitness and activities on a continues basis [1]. This has a long term impact on the recording of health, administration and clinical service to patient’s physiological information. This advancement also helps the provision of more details relating to the daily routine and physical examination. During the health monitoring period, IoT wearable devices are attached with the human body to track the various health metrics that include blood pressure, heart rate, body temperature, respiratory rate, blood circulation level, body pain and blood glucose level [2]. The data collected from the IoT-based wearable devices are stored in a clinical database for necessary action when the patients’ health condition deteriorates. In general, traditional structured query language based databases are used in IoT health monitoring system to store clinical data. There has been an increase in the variety and quantity of IoT-based health monitoring devices in recent times. Hence, the traditional data processing methods and tools are not being used to store sensor data of huge volume generated by various IoT devices [3]. Scalable NOSQL (non structured query language) databases have to be used in the IoT-based health monitoring system. Researchers have started the use of big data and NOSQL technologies in various IoT applications. For example, Hassanalieragh et al.