Energy-efficient rate-adaptive GPS-based positioning for smartphones

Abstract

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy.A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy.In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs celltower-RSS blacklisting to detect GPS unavailability (e.g., indoors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.

Functional Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

“…Others have exhibited framework systems in view of context data, e.g. evaluating and anticipating remote system conditions [10], system steering [35], battery administration [8], [36], and energy proficient GPS obligation cycling [12], [13]. Further, Eagle and Pentland have demonstrated that gadget use examples are in reality organized and unsurprising [37].…”

“…They utilize one or a greater amount of the accompanying three strategies to decrease energy cost, while holding satisfactory execution. To begin with: recurrence decrease, as in [12], [13], [14] diminishes the testing recurrence of energy hungry context sensors. Second: sensor substitution uses lower energy cost context rather than energy hungry ones, as in [10], [12], [13].…”

“…[10]. To address the energy challenge, they frequently utilize impromptu plans along one or a greater amount of these lines: decreasing the recurrence of getting to exorbitant context [12], [13], [14], keeping away from them by and large [10], [12], [13], or substituting them with other context [15], [16], [17], [18].…”

Energy efficient proximity alert on Android

“…In [5], authors argue that using a history of cell-id sequences, one can determine the user's location with accuracy comparable to GPS. In [6] authors utilize the location-time history of the user along with user's past velocity and activity ratio to duty-cycle GPS. In a related vein, in SensLock [7], authors explore the possibility of continuous location tracking in an energy efficient way.…”

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

“…LOBOT requests GPS sampling in an adaptive way that incorporates both location accuracy and energy use. The more frequent GPS sampling likely results in better correction of positioning; but more frequent GPS sampling also means significantly higher cost of power consumption [19], [20], [21]. Roughly, LOBOT adjusts its GPS sampling frequency according to the magnitude of the cumulative error of the local relative positioning.…”