EnAcq: Energy-efficient location data acquisition based on improved map matching

Abstract

With location data becoming an important sensor data resource for a broad range of trajectory-based applications on mobile devices such as vehicle tracking, route navigation, and video tagging, location data acquisition schemes that can reduce the amount of energy spent but still provide accurate location information are essential for these applications' feasibility. This thesis presents EnAcq, a novel energy-efficient location data acquisition scheme based on improved map matching that addresses two key challenges: inaccurate trajectory data and energy consumption. To improve the accuracy of the trajectory data, it utilizes an improved Hidden Markov Model (HMM)-based map matching algorithm which can find candidate matches for each sample point without using a range query and determine the most likely route the vehicle has travelled. To avoid unnecessary energy consumption, it adopts an adaptive GPS sampling method which adjusts the GPS sampling period based on the vehicle's current motion state. Three experiments are performed on a public real-world dataset for evaluating our improved map matching algorithm, adaptive sampling method and proposed EnAcq scheme, respectively. The experimental results show that when the GPS sampling period is not too long, our improved map matching algorithm significantly outperforms a recently proposed HMM-based map matching algorithm in terms of running time. Meanwhile, when compared with sampling at a fixed rate, our adaptive sampling method saves a significant amount of energy, hence prolonging a mobile device's battery life. Furthermore, the results of the third experiment indicate clearly that EnAcq still can provide accurate trajectory data without consuming much energy.