Section: New Results

Characterizing and measuring urban networks

Participants: R. Domga Komguem, M. Fiore, D. Naboulsi, P. Raveneau, R. Stanica, F. Valois

Collection and Analysis of Mobile Phone Data

Cellular communications are undergoing significant evolutions in order to accommodate the load generated by increasingly pervasive smart mobile devices. At the same time, recent generations of mobile phones, embedding a wide variety of sensors, have fostered the development of open sensing applications, such as network quality or weather forecast applications.

In this sense, we contributed with a novel privacy-preserving mobile data collection platform [21] , leveraging the dynamic deployment of crowdsourcing tasks across a pouplation of mobile phones.

Using such data, or other datasets coming from network operators, we can propose dynamic access network mechanisms that adapt to customers' demands. To that end, one must be able to process large amount of mobile traffic data and outline the network utilization in an automated manner. In [28] , we propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. We evaluated our framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months. We showed how our approach allows to classify similar network usage profiles and to tell apart normal and outlying call behaviors.

Generation and Analysis of Vehicular Mobility Datasets

The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset was publicly available that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region.

In [12] , we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400-km2 region around the city of Köln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.

Moreover, using a similar methodology we contribute to the ongoing effort to define such mobility scenarios by introducing a second set of traces for vehicular network simulation, this time focusing on a highway environment. Our traces are derived from high-resolution real-world traffic counts, and describe the road traffic on two highways around Madrid, Spain, at several hours of different working days. We provide a thorough discussion of the real-world data underlying our study, and of the synthetic trace generation process  [20]  [35]  [29] . Finally, we assess the potential impact of our dataset on networking studies, by characterizing the connectivity of vehicular networks built on the different traces. Our results underscore the dramatic impact that relatively small communication range variations have on the network. Also, they unveil previously unknown temporal dynamics of the topology of highway vehicular networks, and identify their causes.

Characterizing Novel Wireless Networks for Urban Intelligent Transportation Solutions

Vehicular networks are not the only contribution communication technologies can bring in the field of Intelligent Transportation Systems. Two other examples have been studied this year in the team.

The first example is related to traffic light control in an urban environment [17] . A traffic light controller takes as input an estimation of the number of vehicles entering the intersection and produces as output a light plan, with the objective to reduce the traffic jam. The quality of the input traffic estimation is a key consideration on the performance of the traffic light controller. The advent of Wireless Sensor Networks, with their relatively low deployment and operation price, led to the development of several sensor-based architectures for intersection monitoring. We show in this work that the solutions proposed in the literature are unrealistic in terms of communication possibilities and that they do not allow a measure of the vehicular queue length at a lane level. Based on extensive experimental results, we propose an energy efficient, low cost and lightweight multi-hop wireless sensor network architecture to measure with a good accuracy the vehicle queue length, in order to have a more precise vision of traffic at the intersection.

On a second example, these last years have witnessed the rise of the smart cities and several mechanisms to render the cities more sustainable and more energy-efficient. Among all different aspects, a noteworthy one is urban bike development. Besides the growing enthusiast provoked by bicycles and the benefit for health they bring, there still exists some reluctance in using bikes because of safety, road state, weather, etc. To counter-balance these feelings, there is a need to better understand bicycle users habits, path, road utilization rate in order to improve the bicycle path quality. In this perspective, in [25] , we propose to deploy a set of mobile sensors on bicycles to gather this different data and to exploit them to make the bike easier and make people want to ride bicycles more often. Such a network will also be useful for several entities like city authorities for road maintenance and deployment, doctors and environment authorities, etc. Based on such a framework, we propose a first basis model that help to dimension the network infrastructure and the kind of data to be real time gathered from bikes. More specifically, we present a theoretical model that computes the quantity of data a bike will be able to send along a travel and the quantity of data a base station should be able to absorb. We have based our study on real data to provide first numerical results and be able to draw some preliminary conclusions and open new research directions.