Mobility-aware cloud services such as fleet management systems need to understand the positions of mobile devices accurately in a real-time manner. Generally speaking, positioning accuracy and data traffic load are in a trade-off relation. Highly accurate real-time positioning requires frequent location data upload and hence results in heavy data traffic load. Although not all data are equally important, data of low importance often consumes a lot of network resources.
This paper presents a data upload control method that the dynamically assesses quality of information (QoI) of measured data at mobile devices. The proposed method balances high accuracy with low traffic loads to achieve efficient vehicle position management. We evaluated the performance of the proposed method using both artificial and actual GPS data and confirmed that it successfully controlled the accuracy and network traffic load according to application requirements.