Characterizing and optimizing background data transfers on smartwatches

Abstract

Smartwatches are quickly gaining popularity, but their limited battery life remains an important factor that adversely affects user satisfaction. To provide full functionality, smartwatches are usually connected to phones via Bluetooth. However, the Bluetooth power characteristics and the energy impact of Bluetooth data traffic have been rarely studied. To address this issue, we first establish the Bluetooth power model based on extensive measurements and a thorough examination of the Bluetooth implementation on Android smartwatches. Then we perform the first in-depth investigation of the background data transfers on smartwatches, and find that they are prevalent and consume a large amount of energy. For example, our experiments show that the smartwatch's battery life can be reduced to one third (or even worse) due to background data transfers. Such high energy cost is caused by many unnecessary data transfers and the energy inefficiency attributed to the adverse interaction between the data transfer pattern (i.e., frequently transferring small data) and the Bluetooth energy characteristics (i.e., the tail effect). Based on the identified causes, we propose four energy optimization techniques, which are fast dormancy, phone-initiated polling, two-stage sensor processing, and context-aware pushing. The first one aims to reduce tail energy for delay-tolerant data transfers. The latter three are designed for specific applications which are responsible for most background data transfers. Evaluation results show that jointly using these techniques can save 70.6% of the Bluetooth energy.