EnAcq

Abstract

Todays versatile mobile devices such as smartphones are increasingly popular platforms for trajectory-based applications such as vehicle tracking, route navigation and geotagged video acquisition. On these battery-powered devices employing a trajectory data acquisition approach that reduces the amount of energy spent but still provides accurate location information is essential for these applications' usability. This paper presents EnAcq, a novel energy-efficient GPS trajectory data acquisition scheme based on improved map matching that addresses two key challenges: providing highly accurate trajectory data and reducing energy consumption. To improve the precision of trajectory data, EnAcq utilizes an improved Hidden Markov Model (HMM)-based map matching algorithm which can find candidate matches for each GPS location sample point without using the traditionally necessary range query and determine the most likely route the mobile device (e.g., in a vehicle) has travelled. To avoid unnecessary energy consumption, EnAcq adopts an adaptive GPS sampling method which adjusts the sampling period based on the device's current motion state. On a public real-world dataset, we demonstrate via experimental results that EnAcq is able to yield accurate trajectory data while avoiding unnecessary energy consumption.