Rich human mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems. Unfortunately, existing mobility datasets--that are available to the research community--are restricted to location data captured through a single sensor (typically GPS) and have a low spatiotemporal granularity. They also lack ground-truth data regarding points of interest and the associated semantic labels (e.g., "home", "work", etc.). In this paper, we present Breadcrumbs, a rich mobility dataset collected from multiple sensors (incl. GPS, GSM, WiFi, Bluetooth) on the smartphones of 81 individuals. In addition to sensor data, Breadcrumbs contains ground-truth data regarding people points of interest (incl. semantic labels) as well as demographic attributes, contact records, calendar events, lifestyle information, and social relationship labels between the participants of the study. We describe the data collection methodology and present a preliminary quantitative analysis of the dataset. A sanitized version of the dataset as well as the source code will be made available to the research community.

The proliferation of mobile devices equipped with internet connectivity and global positioning functionality (GPS) has resulted in the generation of large volumes of spatiotemporal data. This has led to the rapid evolution of location-based services. The anticipatory nature of these services, demand exploitation of a broader range of user information for service personalization. Determining the users' places of interest, i.e. hotspots is critical to understand their behaviors and preferences. Existing techniques to detect hotspots rely on a set of a-priori determined parameters that are either dataset dependent or derived without any empirical basis. This leads to biased results and inaccuracies in estimating the total number of hotspots belonging to a user, their shape and the average dwelling time. In this paper, we propose a parameter-less technique for extracting hotspots from spatiotemporal trajectories without any a-priori assumptions. We eliminate parameter dependence by treating trajectories as spatiotemporal signals and rely on signal processing algorithms to derive hotspots. We experimentally show that, our technique does not necessitate any spatiotemporal or behavior dependent bounds, which makes it suitable to extract hotspots from a larger variety of datasets and across users having disparate mobility behaviors. Our evaluation results on a real world dataset, show accuracy rates exceeding 80% and outperforms traditional clustering techniques used for hotspot detection.

Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to train the prediction models in a centralised server. This amounts to considerably long waiting times before the model kicks in. Disclosing highly sensitive location information to third party entities also exposes the user to several privacy risks. To address these issues, we put forth a mobility prediction system, able to provide swift realtime predictions, evading the strenuous training procedure. We enable this by constantly adapting the model to substantive user mobility behaviours that facilitate accurate predictions even on marginal time bounded movements. In comparison to existing frameworks, we utilise less volumes of data to produce satisfactory prediction accuracies. This in turn lowers the computational complexity making implementation on mobile devices feasible and a step towards privacy preservation. Here, only the predicted location can be sent to such services to maintain the utility/privacy tradeoff. Our preliminary evaluations based on real world mobility traces corroborate our hypothesis.