Abstract

We propose a novel technique for answering routing queries in public transportation networks that allows unrestricted walking. We consider several types of queries: earliest arrival time, Pareto-optimal journeys regarding arrival time, number of transfers and walking time, and profile, i.e. finding all Pareto-optimal journeys regarding travel time and arrival time in a given time interval. Our techniques uses hub labeling to represent unlimited foot transfers and can be adapted to both classical algorithms RAPTOR and CSA. We obtain significant speedup compared to the state-of-the-art approach based on contraction hierarchies. A research report version is deposited on HAL with number hal-02161283.

Abstract

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.

Abstract

International audience; Vehicular networks are one of the cornerstone of an Intelligent Transportation System (ITS). They are expected to provide ubiquitous network connectivity to moving vehicles while supporting various ITS services, some with very stringent requirements in terms of latency and reliability. Two vehicular networking technologies are envisioned to jointly support the full range of ITS services : DSRC (Dedicated Short Range Communication) for direct vehicle to vehicle/Road Side Units (RSU) communications and cellular technologies. To the best of our knowledge, approaches from the literature usually divide ITS services on each of these networks according to their requirements and one single network is in charge of supporting the each service. Those that consider both network technologies to offer multi-path routing, load balancing or path splitting for a better quality of experience of ITS services assume obviously separately controlled networks. Under the umbrella of SDN (Software Defined Networking), we propose in this paper a hybrid network architecture that enables the joint control of the networks providing connectivity to multi-homed vehicles and, also, explore the opportunities brought by such an architecture. We show through some use cases, that in addition to the flexibility and fine-grained programmability brought by SDN, it opens the way towards the development of effective network control algorithms that are the key towards the successful support of ITS services and especially those with stringent QoS. We also show how these algorithms could also benefit from information related to the environment or context in which vehicles evolve (traffic density, planned trajectory, ..), which could be easily collected by data providers and made available via the cloud.

Abstract

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission. Comment: Best Student Paper Award

Abstract

In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion detection) require. It also prevents performance degradation of the SDN switches by keeping the number of flow table entries well below a critical level. This level is not preset as a hard threshold but learned during operation by using a machine-learning based algorithm. The DATA method is implemented within a RESTful application (DATA App) which monitors and analyzes the ongoing network traffic and provides instructions to the SDN controller to adapt the traffic flow matching strategies accordingly. A thorough performance evaluation of DATA is conducted in an SDN emulation environment. The results show that---compared to the default behavior of common SDN controllers---the proposed DATA approach yields significant SDN switch performance improvements while still providing detailed traffic flow information on demand. Comment: This paper was presented at NetSys conference 2019. arXiv admin note: text overlap with arXiv:1909.01544

Abstract

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient. Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 2020

Abstract

In this article we use real traffic data to confirm that vehicle velocities follow Gaussian distribution in steady state traffic regimes (free-flow, and congestion). We also show that in the transition between free-flow and congestion, the velocity distribution is better modeled by generalized extreme value distribution (GEV). We study the effect of the different models on estimating the probability distribution of connectivity duration between vehicles in vehicular ad-hoc networks. Comment: 5 pages, 5 figures, presented at the ICCVE 2014 (International conference on connected vehicles & expo); http://www.iccve.org

Abstract

Traffic congestion is a daunting problem that is affecting the daily lives of billions of people across the world. Recently, a promising new traffic control scheme known as Virtual Traffic Lights (VTL) has been proposed for mitigating traffic congestion. VTL is an infrastructure free traffic control scheme that leverages the presence of Vehicle-to-Vehicle (V2V) communications. Such infrastructure free scheme has several benefits, such as alleviating traffic congestion; reducing the large cost of traffic lights and traffic control systems; reducing carbon emission, etc. This paper reports a DSRC-based prototype design effort on VTL using Dedicated Short Range Communications (DSRC) technology. The experiments performed show the feasibility of implementing VTL with DSRC technology. Preliminary results of the field tests conducted in Pittsburgh with vehicles using VTL equipment indicate that VTL is capable of coordinating traffic at intersections and reducing the commute time of people. Comment: 5 pages, 7 figures Accepted by Vehicular Technology Conference 2018 (2018)

Abstract

The provision of reliable and efficient communication is a key requirement for the deployment of autonomous cars as well as for future Intelligent Transportation Systems (ITSs) in smart cities. Novel communications technologies will have to face highly-complex and extremely dynamic network topologies in a Vehicle-to-Everything (V2X)-context and will require the consideration of mobility information into decision processes for routing, handover and resource allocation. Consequently, researches and developers require simulation tools that are capable of providing realistic representations for both components as well as means for leveraging the convergence of mobility and communication. In this paper, we present a lightweight framework for the simulation of vehicular mobility, which has a communications-oriented perspective by design and is intended to be used in combination with a network simulator. In contrast to existing approaches, it works without requiring Interprocess Communication (IPC) using an integrated approach and is therefore able to reduce the complexity of simulation setups significantly. Since mobility and communication share the same codebase, it is able to model scenarios with a high level of interdependency between those two components. In a proof-of-concept study, we evaluate the proposed simulator in different example scenarios in an Long Term Evolution (LTE)-context using real-world map data.

Abstract

Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes. Comment: 6 pages, 4 figures, ICC 2018 worksho