A generic IoT architecture for ubiquitous context-aware assessments

Abstract

Ubiquitous learning environments move learners out of a classroom and into the real world, where learners can engage in experiential and tangible learning. These environments setup peer-to-peer networks where learners, teachers, and objects of interest can take part in creating learning scenarios. A key component of such systems is a wireless-enabled edge device augmented with various types of sensors to represent the state of physical objects and environments. Most such current systems are built using traditional Internet technologies that often lead to cumbersome, unreliable, and overly complex designs. This paper presents a novel generic technical architecture for ubiquitous assessment systems based on the Internet of Things (IoT) computing paradigm. A commonly used IoT edge device was used to implement four variants of the proposed architecture. The variants were based on Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), and Extensible Messaging and Presence Protocol (XMPP). The four architectural variants were evaluated in terms of power consumption and CPU utilization as the system changes in scale. The variants were also evaluated under various network conditions in order to assess their effect on response time, which in turn influences the user experience. The evaluation revealed that while the MQTT-based implementation demonstrated a consistent, generally-better performance, in practice, all variants of the architecture have a similar resource footprint for this class of applications. Hence, an implementation of the proposed architecture using either of the four protocols is expected to enhance the learning experience by capturing the benefits of ubiquitous and context-aware learning at a low cost, making it ideal for resource-constrained learning environments.