No abstract

Abstract-Different research communities varying from telecommunication to traffic engineering are working on problems related to vehicular traffic congestion, intelligent transportation systems, and mobility patterns using information collected from a variety of sensors. To test the solutions, the first step is to use a vehicular traffic simulator with an appropriate scenario in order to reproduce realistic mobility patterns. Many mobility simulators are available, and the choice is usually done based on the size and type of simulation required, but a common problem is to find a realistic traffic scenario. In order to evaluate and compare new communication protocols for vehicular networks, it is necessary to use a wireless network simulator in combination with a vehicular traffic simulator. This additional step introduces further requirements for the scenario. The aim of this work is to provide a scenario able to meet all the common requirements in terms of size, realism and duration, in order to have a common basis for the evaluations. In the interest of building a realistic scenario, we decided to start from a real city with a standard topology common in mid-size European cities, and real information concerning traffic demands 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 an overview of its possible use cases.

No abstract

No abstract

No abstract

Abstract. 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 di↵erent activities and associated physiological patterns. Next, the use of a scalable and energy-e cient 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.

No abstract

Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score.