LALS: A Low Power Accelerometer Assisted Location Sensing technique for smartphones

Abstract

Advances in location tracking sensors in smartphones have led to the emergence of many location-based services (LBS). Continuous use of these location sensors improves the reliability and accuracy when identifying a user's location, but results in quicker battery depletion due to high energy consumption. In this paper, we present an energy-efficient location determination method for smartphones named Low Power Accelerometer Assisted Location Sensing (LALS). LALS is a high-availability hybrid technique that combines the use of GPS, Wi-Fi Positioning System (WPS), GSM Positioning System (GSMPS) and accelerometer. The novelty of our method is threefold. First, it involves extracting 6 features (5 novel and 1 derived) from the embedded smartphone accelerometer data without need for accelerometer noise filtering. Second, it provides real-time smartphone based user activity classification with a time constraint of 2 seconds avoiding the need to use a remote link to an in-network activity state analyzer. The user activities are stationary, sitting, lying down, standing, walking, jogging, cycling, and motorized movement (travel by bus, overhead train, underground train, taxi, and car). Third, it detects a user's activity transition, promoting a more energy-efficient location sensor selection algorithm. Results show LALS can achieve energy-savings of up to 53% in a typical commuter scenario without compromising on the location accuracy as compared to combinations of GPS, and WPS or GSMPS.