On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices

Abstract

Abstract. An important feature of a modern mobile device is that it can position itself and support remote position tracking. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays and changing positioning conditions. Previous work has established dynamic tracking systems, such as our EnTracked system, as a solution to address these issues. In this paper we propose a responsibility division for position tracking into sensor management strategies and position update protocols and combine the sensor management strategy of EnTracked with position update protocols, which enables the system to further reduce the power consumption with up to 268 mW extending the battery life with up to 36%. As our evaluation identify that classical position update protocols have robustness weaknesses we propose a method to improve their robustness. Furthermore, we analyze the dependency of tracking systems on the pedestrian movement patterns and positioning environment, and how the power savings depend on the power characteristics of different mobile devices.

Energy-efficient trajectory tracking for mobile devices

“…Figure 6 compares acceleration variance to the traveling speed for different transportation modes. The above mentioned problem is illustrated in the plot, since the acceleration variance for biking and driving may be below the variance threshold of twenty identified for pedestrian in our earlier work [11]. However, the plot also indicates that the proposed speed guard allows separating the biking and especially driving data from the pedestrian data, and therefore enables to prevent the usage of the movement-aware strategy with these transportation modes.…”

“…Our work builds on the EnTracked system [13], which focused solely on position tracking using accelerometer based movement detection together with an energy model to determine when to sample the device's integrated GPS receiver. The EnTracked system has been later extended with position update protocols which intelligently determine when to send position updates to a remote server [11].…”

“…When estimating the power consumption the strategies take into account platform-specific variations in [13]. For example, using the internal GPS for obtaining a single position estimate requires on average 11660 millijoules on Nokia N95 devices, whereas the same task can be accomplished using on average only 1530 millijoules on a Nokia N97 device [11] -mostly due to a large difference in the respective GPS power-off delays.…”

“…The power models have been derived with the help of the Nokia Energy Profiler. 3 The process for generating the power models is described in [13] and the parameter values of the power models for the Nokia N97 are described in [11].…”

“…As part of the experiments, we compare our system against two published systems, EnTracked [11,13] and the system of Lange et al [15]. We also consider a dead reckoning (DR) based location update protocol while evaluating the performance of our system for simultaneous position and trajectory tracking.…”