Characterizing Driving Environments Through Bluetooth Discovery

Scientific Conference (2017, October)

Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore ... [more ▼]

Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore, having knowledge of nearby Bluetooth devices, both inside and outside other vehicles, can provide the listening vehicles with enough data to learn about their environment. In this paper, we collect and analyze a dataset of Bluetooth Classic (BC) and Low Energy (BLE) discoveries. We evaluate their respective characteristics and ability to provide context-aware information from a vehicular perspective. By taking a look at data about the encountered devices, such as GPS location, quantity, quality of signal and device class information, we infer distinctive behaviors between BC and BLE relative to context and application. For this purpose, we propose a set a features to train a classifier for the recognition of different driving environments (i.e. road classes) from Bluetooth discovery data alone. Comparing the performance of our classifier with different sampling parameters, the presented results indicate that, with our feature selection, we are able to predict with reasonable confidence up to three classes (Highway, City, Extra-Urban) by using only discovery data and no geographical information. This outcome gives promising results targeted at low energy and privacy-friendly applications and can open up a wide range of research directions. [less ▲]

How Mobile Phone Handovers reflect Urban Mobility: A Simulation Study

in Proceedings of the 5th IEEE Conference on Models and Technologies for Intelligent Transportation Systems. (2017, June 26)

Smartphone-based Adaptive Driving Maneuver Detection: A large-scale Evaluation Study

in IEEE Transactions on Intelligent Transportation Systems (2017)

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors ... [more ▼]

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors to identify individual driver behavior and risky maneuvers. However, in order to estimate driver behavior with smartphones, the system must deal with different vehicle characteristics. This is the main limitation of existing sensing platforms, which are principally based on fixed thresholds for different sensing parameters. In this paper, we propose an adaptive driving maneuver detection mechanism that iteratively builds a statistical model of the driver, vehicle, and smartphone combination using a multivariate normal model. By means of experimentation over a test track and public roads, we first explore the capacity of different sensor input combinations to detect risky driving maneuvers, and we propose a training mechanism that adapts the profiling model to the vehicle, driver, and road topology. A large-scale evaluation study is conducted, showing that the model for maneuver detection and scoring is able to adapt to different drivers, vehicles, and road conditions. [less ▲]

Towards Privacy-Neutral Travel Time Estimation from Mobile Phone Signalling Data

in Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2) (2016, September)

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these ... [more ▼]

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these applications are mobile subscriber dwell times. They express how long users stay in the service range of a base station. In this paper, we want to evaluate whether dwell time distributions can serve as predictors for road travel times. To this end, we transform floating car data into synthetic dwell times that we use as weights in a graph-based model. The model predictions are evaluated using the floating car ground truth data. Additionally, we show a potential link between handover density and travel times. We conclude that dwell times are a promising predictor for travel times, and can serve as a valuable input for intelligent transportation systems. [less ▲]

Luxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation

in IEEE Intelligent Transportation Systems Magazine (2016)

Both the industrial and the scientific communities are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected ... [more ▼]

Both the industrial and the scientific communities are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sources. Usually, a vehicular traffic simulator, with an appropriate scenario for the problem at hand, is used to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is made based on the type of simulation required, but a common problem is finding a realistic traffic scenario. The aim of this work is to provide and evaluate a scenario able to meet all the basic requirements in terms of size, realism, and duration, in order to have a common basis for evaluations. In the interest of building a realistic scenario, we used information from a real city with a typical topology common in mid-size European cities, and realistic traffic demand and mobility patterns. In this paper, we show the process used to build the Luxembourg SUMO Traffic (LuST) Scenario, and present a summary of its characteristics together with our evaluation and validation of the traffic demand and mobility patterns. [less ▲]

A Car Hacking Experiment: When Connectivity meets Vulnerability

in Globecom Workshops (GC Wkshps), 2015 IEEE (2015, December)

Interconnected vehicles are a growing commodity providing remote access to on-board systems for monitoring and controlling the state of the vehicle. Such features are built to facilitate and strengthen ... [more ▼]

Interconnected vehicles are a growing commodity providing remote access to on-board systems for monitoring and controlling the state of the vehicle. Such features are built to facilitate and strengthen the owner’s knowledge about its car but at the same time they impact its safety and security. Vehicles are not ready to be fully connected as various attacks are currently possible against their control systems. In this paper, we analyse possible attack scenarios on a recently released all-electric car and investigate their impact on real life driving scenarios. We leverage our findings to change the behaviour of safety critical components of the vehicle in order to achieve autonomous driving using an Open Vehicle Monitoring System. Furthermore, to demonstrate the potential of our setup, we developed a novel mobile application able to control such vehicle systems remotely through the Internet. We challenge the current state-of-the-art technology in today’s vehicles and provide a vulnerability analysis on modern embedded systems. [less ▲]

Adaptive Activity and Context Recognition using Multimodal Sensors in Smart Devices

in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Mobile Computing, Applications, and Services (2015, November)

The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and ... [more ▼]

The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today's smartphones have the potential to monitor and record a complete history of their owners' movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect different activities and associated physiological patterns. Next, the use of a scalable and energy-efficient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence. [less ▲]

Bluetooth Low Energy performance and robustness analysis for Inter-Vehicular Communications

in Ad Hoc Networks (2015)

Demo: Using Wearables to Learn from Human Dynamics

in Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '15) (2015, May)

Recent technological advances have allowed the development of miniaturized sensors and the emergence of a wide range of connected objects. Whether it's smartphones or in the broader sense wearables, the ... [more ▼]

Recent technological advances have allowed the development of miniaturized sensors and the emergence of a wide range of connected objects. Whether it's smartphones or in the broader sense wearables, the diversity of these devices and their accessibility opens up new fields for applications in the computer sciences. Smartwatches, which are experiencing a boom on the market, will be integral to the research that will shape the Internet in the years to come, namely big data, sensing systems and human behavior. Our demonstration falls within this context and aims to demonstrate the potential of these emerging technologies to respond to problems and to way of thinking introduced by industry and the scientific community, which are generally limited to smartphone sensing frameworks. Further, we plan to present our research platform, SWIPE, which is dedicated to collecting, studying and learning about human dynamics by means of an ecosystem of wearables. [less ▲]

A Cell Dwell Time Model for Synthetic Population Generation from Call Detail Records

Scientific Conference (2015, April 08)

In this work we propose a novel Cell Dwell Time Model that can be used to generate a synthetic population. We introduce two new metrics to define the attractivity of cell sites based on global and ... [more ▼]

In this work we propose a novel Cell Dwell Time Model that can be used to generate a synthetic population. We introduce two new metrics to define the attractivity of cell sites based on global and individual parameters obtained via the analysis of the Data For Development (D4D) Call Detail Records (CDR). We rely on the shortest path road network to interconnect two distant cell sites. The resulting dwell time model can be used to compute accurate user trajectories even with partial information. This work represents a first step towards the generation of a synthetic population that can be used to perform a wide range of simulative studies to evaluate and optimize transportation networks. [less ▲]