DrIVE Associated Team: Distributed Intelligent Vehicular Environment – Enabling ITS through programmable networks

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 DrIVE Objectives

Transportation systems are a key component of our society’s critical infrastructure and are expected to experience transformative changes during the current “information age”. A noteworthy example is the automotive industry which has been disrupted by technologies such as vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication.
Vehicular communication is expected to be one of the key technological enablers of next-generation transportation systems, also known as Intelligent Transport Systems (ITS). In ITS, vehicles exchange information to self-drive, coordinate road traffic, communicate road conditions, avoid accidents, as well as support infotainment services.

ITS services and applications pose significant challenges due to their stringent low latency, reliability, scalability, and geographic decentralization requirements. Leveraging the emergence of the Software-Defined Networking (SDN) paradigm, Software-Defined Vehicular Ad hoc NETwork (SD-VANET) architectures have been proposed as a way to address such requirements. SD-VANETs rely on the separation between network control and data planes, resulting in increased network programmability that
enables vehicles to react and adjust to dynamically changing environmental- and networking conditions. They have demonstrated the benefits of using SDN’s decoupling of network control from data forwarding when compared to “traditional” VANET architectures (e.g., employing multi-hop ad hoc network routing. However, SD-VANETs and other existing solutions either: (1) rely on logically centralized control plane, or (2) use a static control distribution approach, both of which are not compatible with ITS’ QoS needs.

We contend that ITS’ stringent scalability, latency, reliability, and decentralization requirements call for a distributed and flexible network control plane, decoupled from the data plane, that can automatically and dynamically adjust to current environment and network conditions. As such, the main objectives of the DrIVE associated team are to:

  • Develop a programmable network control plane that will dynamically adjust to current environment conditions and network characteristics to support ITS’ scalability, quality of service (QoS), and decentralization requirements, and
  • Apply the proposed distributed network control plane framework to ITS services and applications, such as road hazard warning, autonomous- and self-driving vehicles, and passenger-centric services (e.g., infotainment and video streaming).

Work Plan

The following activities will be launched in the project’s first year and will be carried out concurrently in subsequent years.

  • Vehicle-controller discovery: Our preliminary framework allows vehicles to establish a connection with a controller to exchange control and data packets. Even though we have not yet explored how such connection can be established dynamically, it is an important challenge that we plan to address. OpenFlow allows multiple controller connections for a single switch with the restriction of defining a single controller in charge (or a master controller). However, it does not handle dynamic switch-controller association.
  • Vehicle-to-vehicle communication: One of the challenges is to provide the controller with sufficient information to control a gateway in a secure way. For instance, the controller should be aware of nodes in the gateways’ vicinity, including their position. Such information would enable the controller to define message relevance areas, i.e., decide which nodes should receive which data messages. Secure authentication mechanisms between controllers, switches and gateways will be developed.
  • Handling mobile controllers: Disconnections between vehicles and controllers may happen very often in ITS due to mobility, RSU coverage, etc. One way to mitigate control plane disconnections is to use mobile controllers. For example, a gateway could act as a controller through a delegation process, i.e., a higher layer controller could offload tasks to the new instantiated controller.
  • Instrumentation and monitoring: Allowing controller(s) to get an accurate view of available network resources (e.g., link bandwidth, switch capacities) and flow requirements in a dynamic way and with low overhead is critical. Indeed, controller(s) need to have a precise and up to date vision of network resources in order to make optimal or close-to-optimal decisions and place the NFV at the most strategic locations in the network. It is essential to identify possible bottlenecks in a timely manner, yet ensure monitoring does not impact data plane performance.
  • Prototyping and testing: We will assess the performance of our proposed algorithms and protocols using network simulation (e.g., ns-3), emulation (e.g., mininet-wifi), and reproducible experimentation using testbeds. Some extensions of these evaluation tools and platforms may have to be developed to allow experimenting with ITS-like scenarios.

Work Plan for 2020

  • Explore machine intelligence mechanisms for control plane delegation based on controller load prediction and latency optimization objectives.
  • Machine learning will be applied to the prediction of routes (car trajectory) towards faster datapath handover and service pre-provisioning at new, closeby edge computing facilities.
  • MEC assignment problem will be explored driven by latency-sensitive ITS application dataplane functions (e.g., < 20ms). Service latency optimization will be explored with centralized and distributed controller assignment approaches.
  • Prototyping and testing. We plan to integrate Mininet-WiFi within R2lab to extend the range of possible ITS scenarios in R2lab. Mininet-WiFi will continue to be extended as needed by the target ITS experiments. We foresee developments in integration with Google maps, import /export of traces, enhanced instrumentation to stream metrics in a telemetry-like fashion.

Scientific Progress

        2019:

  • We have written a comprehensive survey that explores the current state of the art of how Machine Learning (ML) technology has been applied to a broad range of ITS applications and services, such as co-operative driving and road hazard warning. We also identified future directions on how Intelligent Transport Systems (ITS) can benefit from ML technology. The survey is under submission in a journal, see [JR] https://hal.inria.fr/hal-02284820.
  • Software Defined Vehicular Network (SDVN) has highly dynamic network topology due to high mobile vehicles. Delay-sensitive VANET applications (e.g., self-driving), drive the urgency to offer low control latency with mobility support. We are exploring multi-agent Deep Reinforcement Learning (DRL) to provide dynamic controller assignment in SDVN with mobility support. A paper is expected to be submitted to a journal in Nov. 2019.
  • Unmanned aerial vehicles (UAVs) can play various roles in ITS, such as aerial delivery. We are investigating reinforcement learning techniques to optimize the 3D placement of UAVs to balance the utilization of terrestrial links. The work is expected to be submitted to a conference in January 2020.
  • We have been developing a dynamically distributed and decentralized  network control plane framework to support autonomous or semi-autonomous driving. The proposed framework is based on a hierarchy of controllers in which local controllers are placed closer to vehicles in order to better satisfy the different quality-of-service requirements of cooperative driving services (e.g., ultra low latencies) as well as adapt to dynamic network- and environment conditions. We are currently preparing a journal submission which, along with the proposed control plane architecture, will also discuss our current implementation, as well as extensive experiments using realistic scenarios.
  • Prototyping and testing:
    • As an example of ITS scenario leveraging ns-3 integration within R2lab, we designed a V2I scenario in which a (mobile) car is communicating with an RSU within ns-3 and where the RSU is connected to the infrastructure with a real LTE link running on R2lab. This scenario has been used to evaluate a new video streaming mechanism in paper [4] that will be presented at the CCNC conference in January 2010 at Las Vegas.
    • Mininet-WiFi is being extended to allow experiments with real mobility traces from buses in the city of Rio de Janeiro and de UNICAMP campus. Realistic models for 5G-like cellular coverage, RSU placements, SDN controllers, and MEC servers are being worked out into flexible topologies for realistic experiments of moving cars attaching to different points in the network and communicating with different ITS applications in the edge and core clouds, yielding different application-level performance metrics.

        2018:

  • Message dissemination in ITS: We presented at the IEEE/ACM DS-RT 2017, a first version of D2-ITS, a flexible and extensible framework to dynamically distribute network control in order to enable message dissemination in ITS [J2]. D2-ITS uses a distributed control plane based on a hierarchy of controllers that can dynamically adjust to environment and network conditions in order to satisfy ITS application requirements. We demonstrate the benefits of D2-ITS through a proof-of-concept prototype using the ns-3 simulation platform. Our preliminary results show that D2-ITS yields lower message delivery latency with minimal additional overhead.
  • Network planning and provisioning:  We proposed a framework that optimizes the deployment cost of 5G networks. It is based on a Mixed Integer Quadratically Constrained Programming (MIQCP) model that optimizes the deployment cost of 5G network functions while performing adequate provisioning to address user demand and performance requirements. We use two realistic scenarios to showcase that our framework can be applied to different types of deployments. This work has been presented at the IEEE ICC 2018 [J1].
  • Prototyping and testing: We have also been developing a variety of platforms including  network testbed, emulator, and simulator to make it possible to evaluate ITS communication mechanisms developed within DrIVE in realistic, yet reproducible scenarios:
    • R2lab testbed: we have enhanced the performance of the experiment control tool nepi-ng that it used to easily create and perform wireless scenarios within the testbed. The tool has been demonstrated at the ACM Mobicom Wintech 2018 workshop and a paper has been presented there too [2,3]. Also, we have proposed a way to mix experimentation and simulation in the same scenario to enable for instance real wireless transmissions between R2lab nodes and mobile simulated nodes running on the ns-3 simulator [1].
    • Mininet-Wifi network emulator: we have implemented two main extensions to the Mininet-Wifi network emulator – (1) Wireless nodes or vehicles with access point association can report their connectivity status to the SDN controller in terms of signal strength and other parameters. This new feature will be used for handover of vehicles which can be initiated by an SDN controller or by the vehicle itself; (2) Inter-controller communication through a publish-subscribe interface to allow state exchange between SDN controllers. Using publish-subscribe communication will facilitate different communication models, e.g., one-to-one, one-to-many, many-to-one, many-to-many. In order to add the new Mininet-Wifi functionality described above, we integrated tools and technologies such as Scapy and WPA Supplicant. Scapy is used for transmission and manipulation of 802.11 beacons. WPA Supplicant allows control of roaming nodes and their association in a wireless network based on IEEE 802.11.

Testbeds and Software


Joint Publications


Related Publications

  • [5] Houssam Elbouanani, Chadi Barakat, Guillaume Urvoy-Keller, Dino Lopez-Pacheco, “Collaborative Traffic Measurement in Virtualized Data Center Networks“, Poster at IEEE CloudNet’19, 4-6 Nov., Coimbra, Portugal.
  • [4] K. Matsuzono, H. Asaeda, Indukala Naladala, Thierry Turletti, “Efficient Pull-based Mobile Video Streaming leveraging In-Network Functions“, IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Jan 2020, Las Vegas, USA.
  • [3] Thierry Parmentelat, Thierry Turletti, Walid Dabbous, Naoufal Mahfoudi, Francesco Bronzino, “nepi-ng: an efficient experiment control tool in R2lab“, ACM WiNTECH 2018 – 12th ACM International Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization, Nov 2018, New Delhi, India. pp.1-8.
  • [2] Yonathan Bleyfuesz, Thierry Parmentelat, Thierry Turletti, Farzaneh Pakzad, Mohamed Mahfoudi, Walid Dabbous, “Demo: Using nepi-ng for Mesh Networks Experiments“, The 12th ACM International Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (WINTECH), Nov 2018, New Delhi, India.
  • [1] Indukala Naladala, “Integrating R2lab with ns-3” B.T. in CS and Engineering Internship report, NITK Mangalore, July 2018.

Related Thesis/Reports

  • Houssam Elbouanani (M.Sc. Ubinet UCA 2019), M.Sc. thesis “Measurement as a Service in Modern Data Centers“, August 2019.
  • Mohamed Naoufal Mahfoudi, Thierry Parmentelat, Thierry Turletti, Walid Dabbous, Towards Reproducible Wireless Experiments Using R2lab, Research Report, June 2019.
  • Y. Bleyfuesz,  (Master 1 Informatique UNSA 2018) on Experimental Comparison between OLSR and BATMAN protocols using R2lab.
  • Indukala Naladala (B.T. in CS and Engineering, NITK Mangalore 2018) on Integrating R2lab with ns-3.

Visits between Partners

  • Oct 21 – Nov 8, 2019: Tingting Yuan 3-weeks visit at UNICAMP
  • Oct 21-24, 2019: meeting at UNICAMP (visit of Tingting Yuan and Thierry Turletti)
  • July 10-12, 2019: meeting at Inria (visit of Christian Esteve Rothenberg and Katia Obraczka)
  • December 3-7, 2018: meeting at UNICAMP (visit of Katia Obraczka and Thierry Turletti)
  • July 17-31, 2018: Katia Obraczka 2-weeks visit at Inria.

Participants

DIANA at Inria Sophia Antipolis, FRANCE:

  • Thierry Turletti (PI): Senior Researcher. Network architectures, Wireless and Testbeds skills
  • Chadi Barakat: Senior Researcher. Measurement, QoE and network modeling skills
  • Walid Dabbous: Senior Researcher, head of DIANA team. Network architectures and Testbeds skills
  • Thierry Parmentelat: Senior Engineer, working on the R2lab testbed
  • Osama Arouk (up to September 2018): Postdoc Labex@UCN on 5G NFV slicing.
  • Tingting Yuan (from Nov 2018): Postdoc Inria on Software-Defined VANETs.
  • Naoufal Mahfoudi (up to Oct 2019): PhD student, with expertise on experimentation in the R2lab wireless testbed.
  • Houssam Elbouanani (starting in Dec 2019): PhD student, with expertise on network monitoring.
  • Houssam Elbouanani (Mar – Aug 2019): M.Sc. student from UCA Ubinet on traffic measurement, see [5].
  • Indukala Naladala: (June-July 2018) B.T. student from NITK Mangalore, 2-months internship on integrating ns-3 to R2lab, see [4].
  • Adeel Khalid Siddiqui: (Nov – Dec 2019) M.Sc student from Ubinet UCA, France, PFE on integrating mininet-wifi within R2lab.

INTRIG at UNICAMP, Campinas-SP, BRAZIL :

  • Christian Esteve Rothenberg (PI): Assistant Professor, head of  INTRIG team.  Wireless, Security and Network architectures skills.
  • Ramon Dos Reis Fontes: Postdoc, Wireless SDN and mininet-wifi skills.
  • Gyanesh Kumar Patra: PhD student, control software design for networking and SDN skills.
  • Wilson Borba da Rocha Neto: PhD student, ..
  • Daniel Harrison: MSc student, ITS skills.

Ericsson Research, Indaiatuba-SP, BRAZIL (Satellite Partner):

  • Mateus Augusto Silva Santos (PI): Experienced Researcher. Ad hoc networks and SDN skills.
  • Pedro Henrique Gomes: Experienced Researcher. Industrial IoT skills.
  • Daniel Harrison: Researcher at Ericsson and regular MSc student at Unicamp since Aug/2017, expected to defend in the 2nd semester of 2019.

UC Santa Cruz, CA, USA (Other Partner):

  • Katia Obraczka (PI): Professor, head of i-NRG team, Network architectures skills.
  • Anuj Kaul: 3rd year PhD student, SDN and ITS skills.
  • Lie Xue: 2nd year PhD student, Security and SDN skills.

 

 

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