Sébastien Faye

A distributed algorithm for multiple intersections adaptive traffic lights control using a wireless sensor networks

In this article, we detail and evaluate a distributed algorithm that defines the green lights sequence and duration in a multi-intersection intelligent transportation system (ITS). We expose the architecture of a wireless network of sensors deployed at intersections, which takes local decisions without the help of a central entity. We define an adaptive algorithm, called TAPIOCA (distribuTed and AdaPtive IntersectiOns Control Algorithm), that uses data collected by this sensor network to decide dynamically of the green light sequences, considering three objectives: (i) reducing the users average waiting time while limiting the starvation probability; (ii) selecting in priority the movements that have the best load discharge potential and (iii) synchronizing successive lights, for example to create green waves. Simulation results performed with the SUMO simulator show that TAPIOCA achieves a low average waiting time of vehicles and reacts quickly to traffic load increases, compared to other dynamic strategies and to pre-determined schedules.

A distributed algorithm for adaptive traffic lights control

In this paper, we address the problem of controlling traffic lights at an intersection with a spatially distributed sensor network. We propose a sensor network architecture that does not depend on a centralized coordinator and we separate logically it into 4 levels of hierarchy. On this architecture, we define and evaluate through simulations an adaptive traffic light control algorithm. Based on two main objectives, this algorithm decides dynamically of the green lights sequences by selecting the movements composing each phase and its duration. Simulation results show that this algorithm, if properly tuned, has the capacity to reduce average waiting time at an intersection, while avoiding starvation.

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

In the near future, wireless networks will be one of the key technologies for road traffic management in smart cities. Vehicles and dedicated roadside units should be interconnected through wireless technologies such as IEEE 802.11p (WAVE). Traffic lights and road signs may also take their place in this architecture, forming a large-scale network of small devices that report measurements, take orders from a control center, and are able to take decisions autonomously based on their local perception. Such a network shares many similarities with classical wireless sensor and actuator networks, starting with its distributed organization and with the role of the control center. However, its topology, and, subsequently, the appropriate selection of protocols and algorithms, will be strongly influenced by each citys characteristics. In this paper, we characterize and discuss probable topologies of these networks. The aim of this work is to provide network models that can be used to evaluate protocols and algorithms using realistic scenarios in place of generic random graphs. We deploy such networks over 52 city maps extracted from OpenStreetMap and characterize the resulting graphs, with particular focus on the connectivity aspects (degree distribution and connected components). The tools, the complete data sets, and OMNeT++ network models are freely available online.

Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

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.

Influence of radio communications on multiple intersection control by a wireless sensor network

In this paper, we describe and enhance TAPIOCA (distribuTed and AdaPtive IntersectiOns Control Algorithm), a distributed algorithm that relies on a wireless sensor network to manage urban road traffic efficiently. We detail TAPIOCA with a special emphasis on communications and we study its reaction to losses and delays induced by the use of wireless communication. We then propose a prediction mechanism that alleviates these issues and show, using co-simulation between SUMO and OMNeT++, that such interpolation mechanisms are effectively able to replace missing or outdated data.

Luxembourg SUMO Traffic (LuST) Scenario: Traffic Demand Evaluation

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.

Toward a Characterization of Human Activities using Smart Devices: A Micro/Macro Approach

The emergence of new connected devices has opened up new opportunities and allowed to imagine concepts that bring computer sciences and social sciences closer together. In particular, todays increasingly sophisticated miniature sensors allow to track and understand human activities and behavior with a great precision. Taking different approaches and perspectives, we use in this paper smartwatches and smartglasses to explore these behaviors and show that these objects, considered by many as gadgets, have an important role to play in understanding the lives of individuals. The main objective of this work is to introduce two new scales of activity detection, which lacks a formal and consistent definition in the literature. First, we propose a model that precisely detects and interprets movements made by a person wearing smart devices. Then, we use this model to show different interactions between those micro-activities and bigger chunks of behaviors we call macro-activities. Using a new concept based on 3D visualization, we finally show that combining those two scales and using a limited dataset, it is possible to distinguish between different individuals when they are performing very similar activities. The findings of this study lead the way to enhanced user profiling.

Demo: Using Wearables to Learn from Human Dynamics

Recent technological advances have allowed the development of miniaturized sensors and the emergence of a wide range of connected objects. Whether its 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 [2, 3]. 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 [1]. 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. A short presentation video is available online at http://swipe.sfaye.com/mobisys15/.

Characterizing User Mobility Using Mobile Sensing Systems

Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.

Human Mobility Profiling Using Privacy-Friendly Wi-Fi and Activity Traces: Demo Abstract

Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a users mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a users profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information. A video showing the different interfaces to be presented is available online.