Real-time Context-aware Learning System for IoT Applications
This addresses the problem of real-time context-aware learning for IoT users on mobile devices, but it is incremental as it builds on existing server-assisted methods.
The paper tackles the challenge of running accurate machine learning on mobile devices for real-time IoT applications by proposing a lightweight context-learning algorithm that updates from a server, achieving 97.51% mean accuracy with 11ms execution time.
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made 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. To solve this issue, some authors have run machine learning algorithms on the server and transmitted the results to the mobile devices. Although the context-aware predictions made by the server are more accurate than their mobile counterpart, 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 work, we describe 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 light-weight context-learning algorithm can achieve mean accuracy up to 97.51% while mean execution time requires only 11ms.