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An IOT-Based Context-Aware Wearable Assessment Platform for Smart Watches
Al Solh, Mohamad Naeem
Al Solh, Mohamad Naeem
Date
2017-11
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Advisor
Type
Project
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Description
A Master of Science project in Computer Engineering by Mohamad Naeem Al Solh entitled, “An IOT-Based Context-Aware Wearable Assessment Platform for Smart Watches”, submitted in November 2017. Thesis advisor is Dr. Imran Ahmed Zualkernan. Soft and hard copy available.
Abstract
The purpose of this research is to build and evaluate an Internet of Things (IoT) architecture that utilizes the Message Queue Telemetry Transport (MQTT) and an end-to-end JavaScript stack with a NoSQL database to process real-time on-board sensor data on smart watches to implement context-aware ubiquitous learning applications. A prototype system was designed `and implemented to serve curriculum-aligned, real-time assessments utilizing smartwatch sensors that represent a learner’s environmental and bodily context. The system integrates a variety of on-board watch sensors such as a Global Positioning System (GPS), a Pedometer, Light Intensity, Ultra-violet Radiation, and a Heart Rate Monitor. An integrated smartwatch program uses JavaScript/HTML5. The JavaScript stack utilizes Node.JS/Express for the middleware and Angular 4 for the teacher administration portal. The system uses Google Classroom as the learning management system, PONTE as the MQTT broker, and CouchDB as the NoSQL database. The performance of the prototype was evaluated on real smart watches under various network conditions (Wi-Fi, 3G, EDGE, and 2G). The backend servers were also assessed for scalability. Without edge
analytics, the average worst-case response time of telemetry submission and acknowledgement (160-second interval and about 95 KB sensor data) was an acceptable 4.5 seconds for the 2G Lossy Rural, and 356 milliseconds for Wi-Fi. Under normal conditions, watch CPU utilization was between 30-90% and never exceeded 98% in the worst case. Watch battery depleted on average 8.64% for a half-an-hour ubiquitous learning session. A typical quad-core laptop running broker, middleware, and database had an average CPU utilization rate of less than 6.25% and the worst case of 25% for serving eight physical watches simultaneously. The proposed IoT-based architecture for smartwatches seems to be feasible and scalable for context-aware ubiquitous learning applications. However, the system needs to undergo field-testing and further optimization using edge-analytics. Scalability to 100’s or 1000’s of watches should also be investigated because theoretically the architecture should scale.