Virtualizing Smartphone Applications to the Cloud

Smartphone technologies have enabled sophisticated pervasive applications for mobile users. Still, many intensive applications perform poorly on smartphones due to the shortage of resources for computation, data storage, network bandwidth, and battery capacity. While such applications can be re-designed with client-server models to benefit from subscribed cloud services, the users are no longer in full control of the entire application execution, which has raised a serious concern. Meanwhile, privacy and security are also important issues, and it is an ongoing debate if public cloud services could be trusted with sensitive data. For mobile users to take full advantage of cloud services, these issues need to be resolved. In this paper, we propose an innovative framework for mobile users to execute existing Android applications on a personal virtual phone safely in the cloud. Instead of using a client-server model, the entire virtual phone is mostly controlled by the user to minimize the intervention from the service provider. Virtualization and encryption are employed to protect against eavesdropping from cloud providers and network attackers. To quickly migrate an Android application between the physical phone and the virtual phone, we use a new application-level checkpointing mechanism and minimize the state of the application

Impact of Data Resolution on Peak Hour Factor Estimation for Transportation Decisions

Inductance loop detection systems serve as a primary data source to contemporary traffic information systems. Measures like 20-second or 30-second average velocity, flow, and lane occupancy can be aggregated from individual loop detector actuation sampled at 60 Hz typically. Practically, these measures would sometimes be further aggregated into a much lower, e.g. 15-minute, resolution and then the raw data were lost. Valuable traffic information like flow variation may be distorted when the lower resolution aggregation is practiced. A biased conclusion could be drawn from a data integration system consisted of this kind of distortions. Three approaches estimating a peak hour factor based on traffic volume from loop detection systems are introduced in this paper to explore such a quality issue for data integration systems. Peak hour factor is commonly used in Highway Capacity Manual for determining and evaluating future system needs. By processing the raw data with the introduced approaches, different PHFs can be determined from a same traffic dataset. It is found that 2% to 5% (about one standard deviation from the mean) reduction in PHF may have 5 to 20 seconds increase in control delay estimation. The results suggest that distortion of control delay estimation at a signalized intersection exists due to an improper aggregation. That is, data quality might not be good enough for a right decision if the data were not processed appropriately. © Versita sp. z o.o