We present eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional implementations sample the phone GPS at the highest possible rate (usually 1Hz) to ensure constant highest possible localization accuracy. This practice results in excessive phone battery consumption and reduces the attainable length of a navigation session. The seemingly most common solution would be to always use a car-charger and keep the phone plugged-in during navigation at all times. However, according to a comprehensive survey we conducted, only a small percent of people would actually always carry around their phones' car-chargers and cables, as doing so is inconvenient and defeats the true ''wireless'' nature of mobile phones. In addressing this problem, eNav exploits the phone's lower-energy on-board motion sensors for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint, using actual GPS sampling only when close. Our user study shows that, while remaining virtually transparent to users, eNav can reduce navigation energy consumption by over 80% without compromising navigation quality or user experience.
In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.
We present eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional implementations sample the phone GPS at the highest possible rate (usually 1Hz) to ensure constant highest possible localization accuracy. This practice results in excessive phone battery consumption and reduces the attainable length of a navigation session. The seemingly most common solution would be to always use a car-charger and keep the phone plugged-in during navigation at all times. However, according to a comprehensive survey we conducted, only a small percent of people would actually always carry around their phones' car-chargers and cables, as doing so is inconvenient and defeats the true "wireless" nature of mobile phones. In addressing this problem, eNav exploits the phone's lower-energy on-board motion sensors for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint, using actual GPS sampling only when close. Our user study shows that, while remaining virtually transparent to users, eNav can reduce navigation energy consumption by over 80% without compromising navigation quality or user experience.
Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS ; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1] , however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.
Climate models are useful tools for simulating the uncertainties of climate change under different emission scenarios. Regional Climate Models are high resolution climate models which generate high-dimensional spatio-temporal output. To effectively summarize such output without subsampling is important but difficult. One important aspect in climate assessment is the characteristic of extreme precipitation events and m-year precipitation return values are often computed as the summary statistics of the extreme precipitation events. In this paper we present a Penalized Maximum Likelihood (PML) method to estimate precipitation return values with Generalized Extreme Value distribution (GEV). With PML models, we have a different set of GEV parameters at each spatial location and we add smoothness penalties on parameters based on prior belief that the neighboring parameters should vary smoothly. The penalization terms are selected by data-driven approaches. We evaluate the uncertainty of the estimates using pointwise standard deviations.
Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests. Identifying the sentiments of these social media data can help users make informed decisions. Existing research mainly focus on developing algorithms by mining textual information in social media. However, none of them collectively consider the relationships among heterogeneous social entities. Since users interact with social brands in social platforms, their opinions on specific topics are inevitably dependent on many social effects such as user preference on topics, peer influence, user profile information, etc. In this paper, we present a systematic framework to identify sentiments by incorporating user social effects besides textual information. We apply distributed item-based collaborative filtering technique to estimate user preference. Our experiments, conducted on large datasets from current major social platforms, such as Facebook, Twitter, Amazon.com, and Flyertalk.com, demonstrate that incorporating those user social effects can significantly improve sentiment identification accuracy.
The use of social media to report and track events of significance is being widely adopted by individuals. These social media reports are tagged with metadata that are rich sources of information. In this paper, we are interested in the space-time metadata and use these to model the spread of events in space and time. In particular, we illustrate the spread of one particular event-gas shortage in the aftermath of Hurricane Sandy. We show that classical overload failure models (used in modeling cascading failures in smart power grids) and epidemiological models (used in modeling the spread of infectious diseases) are inaccurate in modeling such an event and develop new models to accurately capture the spread of this event. We evaluate the accuracy of our model using over 2 million tweets collected over a period of 22 days and show that we perform significantly better than standard epidemiological models.
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