Energy-efficient trajectory tracking for mobile devices

Abstract

Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, when trajectory information needs to be sent to a remote server, on-device simplification of the trajectories is needed to reduce the amount of data transmission. While there has recently been a lot of work on energyefficient position tracking, the energy-efficient tracking of trajectories has not been addressed in previous work. In this paper we propose a novel on-device sensor management strategy and a set of trajectory updating protocols which intelligently determine when to sample different sensors (accelerometer, compass and GPS) and when data should be simplified and sent to a remote server. The system is configurable with regards to accuracy requirements and provides a unified framework for both position and trajectory tracking. We demonstrate the effectiveness of our approach by emulation experiments on real world data sets collected from different modes of transportation (walking, running, biking and commuting by car) as well as by validating with a realworld deployment. The results demonstrate that our approach is able to provide considerable savings in the battery consumption compared to a state-of-the-art position tracking system while at the same time maintaining the accuracy of the resulting trajectory, i.e., support of specific accuracy requirements and different types of applications can be ensured.

Managed Participatory Sensing with YouSense

“…Trajectory tracking instead of location tracking can be used to detect visits to areas of interest with less energy use (Kjaergaard et al, 2011), and vibration circuits in future mobile phones may lead to more efficient motion detection (Guha et al, 2010).…”

Pocket Caching: A Strategy of Prefetching Cache Based on Multi-objective Optimization for Mobile Cloud Computing

“…Prediction of user path: Some works in the literature such as Henao in [11], Hariharan in [12], Liao in [13], Kang in [14] and Kjaergaard in [15] give ideas to identify user path. They propose mechanisms to detect patterns of user behavior based on places (e.g.…”

Group-based Motion Detection for Energy-Efficient Localisation

“…Guo et al [21] have used accelerometers, high sample rate GPS, and magnetometers to develop a cattle movement and behaviour model on which this paper builds. Kjaergaard et al [22] use both accelerometers and magnetometers for energy efficient tracking, and consider individual duty cycling strategies for these components. Our approach differs from these works in that it considers group based duty cycling strategies for accelerometers or other motion detection sensors.…”