A Context-Aware Scheduling Mechanism for Smartphone-Based Personal Data Collection from Multiple Wearable Devices

Due to rapid progresses of smartphone and various wearable devices, it becomes feasible to collect rich personal data that can be used for activity recognition, user modeling and better personalized services. Because of the popularity and high accessibility, a smartphone becomes not only an effective terminal in personal data collection but also a gateway to connect wearable devices and gather various kinds of personal data from these wearables. In the most of current applications, the wearables work for data collection according to a fixed schedule often preset manually by a user. The main problems in the data collection with following such fixed scheduling are weak adaptiveness to wearables' state change, big redundancy in collected data, and possible mismatch to dynamic precision requirements in data capture. Therefore, we propose a context-aware scheduling mechanism that is able to dynamically adjust the data collection schedule based on varying situations of wearable condition, network availability, computing resource and user state. This paper presents the details of this context-aware scheduling mechanism, and a corresponding smartphone-based system to collect personal data from multiple wearables and upload the gathered data to a server. The efficiency and effectiveness of the proposed scheduling mechanism have been verified by the actual data collection using the developed system.