Leveraging edge analysis for Internet of Things based healthcare solutions

The Internet of Things (IoT) and its applications give rise to an unprecedented amount of data that requires transfer, processing/analysis and storage. Often times, the norm is to maximize the capability of cloud computing services and infrastructure in running complex algorithms on data associated with IoT devices, in order to extract useful information, as well as harnessing its storage services. The downsides of employing cloud computing services include unacceptable latency, high storage cost, increased network bandwidth, etc. However, edge computing eliminates the problems of data communication latency of cloud computing, by processing data at its source. Thus, devices not continuously connected to the Internet can be used for data processing and analysis. Also due to the heterogeneous nature of devices used in data collection, the necessary data integration prior to analysis poses a challenge to developers of IoT solutions and products. The use of a middleware platform was harnessed to solve this problem. In this paper, we employ the use of an edge computing platform for analyzing data generated by IoT devices used in providing healthcare services. This is especially important as such services require real-time analytical results for health monitoring and diagnosis. The system depicted in this work is based on a proposed patient-centric architecture for remotely monitoring patients, and comprises of a biosensor, a smartphone, and an edge analytics platform for data analysis. The biosensor (heart rate sensor) used for this work collects physiological data from the patient and sends it via Bluetooth to the smartphone, which hosts a mobile application, “HealthMate”, that pre-processes the sensor data, transfers it to the edge computing platform server, Kaa server, for further data analysis and storage. The analyzed sensor data is sent back to the mobile application on the smartphone for real-time visualization if required.