Power Conservation in Cloud-Assisted Real-Time Context Learning System

Contextual information can be learned at the mobile devices, such as smartphones, in real-time from the sensors to provide better services to the user. The sensor data collection process, where the data is collected by various internal sensors or autonomous external sensors, incurs greater power consumption, depending upon the type of sensor and data capturing rate in the mobile devices. On the other hand, the learning process itself drains the battery and at the same time affects the accuracy of the learning due to the limited computational power of the mobile devices. These problems can be addressed by shifting the learning process to the cloud, which is however achieved at the cost of reducing the accuracy of the real-time solutions and incurs heavy bandwidth usage depending upon the context of the user. Therefore, we propose a cloud-based real-time context-learning system where the user of the system will get the predetermined service in real-time according to the userdetermined context, which is learned from the related sensors while conserving a maximum amount of power compared to the standalone system or the cloud-based system. We have produced experimental results using a smartphone that illustrates that our system conserves 96.36% of power compared to the mobilelearning system while at the same time, the network data usage is 80% lower when compared to the cloud-based system. We have also showed that the proposed system works 76.17% and 94.81% faster compared to the mobile-learning and cloud-based system respectively.