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Adaptive GPS Duty Cycling

As the quantity and quality of sensors in smartphones increase, smartphones are emerging as promising platforms with lots of potential applications. Sensors allow applications that run on the phone to identify the context of the user, and adjust settings or perform operations accordingly. However, the applicability of most of these context-aware systems is challenged by the limited amount of energy that the phone battery can provide compared to the energy requirements of sensors. Our project tries to tackle this issue by focusing on the GPS as an example of an energy-intensive sensor. First, we propose a probabilistic method to predict the time when the GPS should be triggered ON, based on weekly offline analysis of the GPS traces of the user. Next, we propose a method to decide when to take the next GPS sample, based on the user’s current activity and historical GPS traces. Informally speaking, the GPS is sampled more frequently when the user is currently changing his location in a non-uniform manner. If the motion of the user is relatively uniform (e.g., the user is moving in a straight line), the GPS is sampled less frequently. We evaluate our heuristics on GPS traces that were recorded in different cities. Moreover, for the purpose of evaluating our heuristics on a real-world application, we develop FriendFavorites, an application that runs on Android phones and detects the favorite places of the user to share them with friends. We show that our heuristics result in longer battery life compared to static duty cycling, while maintaining good localization accuracy. 1. BACKGROUND AND RELATED WORK Existing literature that attempts to reduce the energy consumption of location-based services on smartphones can be categorized under one of the following two categories, namely substitution and optimization.