Real-time Context-aware learning System for IoT Applications

This paper introduces a real-time context-aware learning system that runs on mobile devices, collects data from the sensors, learns about the user-defined context, makes predictions in real-time, and manage IoT devices accordingly. However, the computational power of the mobile devices makes it challenging to run machine-learning algorithms with acceptable accuracy. Hence, existing works implement machine-learning algorithms on the server and transmit the results to the mobile devices. Although the context-aware predictions made by the server are more accurate than their mobile counterpart is it heavily depends on the network connection for the delivery of the results to the devices, which negatively affects real-time context learning. Therefore, in this paper, we propose a context-learning algorithm for mobile devices, which is less demanding on the computational resources and maintains the accuracy of the prediction by updating itself from the learning parameters obtained from the server periodically. Experimental results show that the proposed lightweight context-learning algorithm can achieve mean accuracy up to 97.51% while mean execution time requires only 11ms.