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rootMEC-based Stable Topology Prediction for Vehicular Networks

MEC-based Stable Topology Prediction for Vehicular Networks

Use Case Details:

Attributes

Description

Informational

TypeNewThe use case is proposed under the ICN BP family
Industry sector

Area: SDN & NFV

Company: ATTO Research

University:

Jeju National

Gachon University
Country: Republic of Korea

ATTO RESEARCH focuses
We focus on the problems related to networking and software technology for a better connection.
It has created a
The technology that allows to build IT infrastructure, and aims to grow into a ‘Software Defined Infrastructure’ company.
Business driverThe stable vehicular network is essential to enable various applications such as autonomous driving through VANETs. Proposed MEC architecture tends to enable a promising infrastructure where a stable network topology can be predicted locally to improve the network performance by providing intensive calculation for vehicles in the adjacent roads. Thus, converging the two concepts of MEC and topology prediction can provide a strong use case for the vehicular networks such as proactive path stabilization.The MEC-based Efficient Routing Algorithm can provide a stable path by using the predicted future position for the nearby vehicles. The information can also be made available to the adjacent road resulting from being useful to provide a stable topology on the road tracks.
Business use case
  • Edge cloud deployable at RSUs to support applications such as ML-based location prediction, topology stabilization

Business cost - Initial build

Minimal configuration is three servers in total:

  • Master/Database node (1st server)
  • Edge node 1 (2nd server)
  • Edge node 2 (3rd server)
Price factor depends on the cost of RSU quality, and should be only considered for physical deployment. i.e. wireless or wired.
Business cost - Operational

Virtual environment does not require cost.


Operational need

Using the frontend GUI to:

  • Orchestrate virtual resources
Manage the edge applications

Additional details
  • Support of path within a Single operator domain

PPT is attached as proposal statement.

Species Details:

Attributes

Description

Informational

Type

Integrated Cloud Native NFV/App stack (ICN)


Blueprint Family

Existing


Use case

Stable network topology in IoV


Blueprint proposed nameMEC-based Stable Topology Prediction for Vehicular Networks

Initial POD cost

Satellite POD


Scale & Type

System will be developed/deployed in VMs.


Applications
  • ML model
  • LTE network services
  • ProSe Functions

Open Air Interface (OAI) provided LTE network services will be used.

Infrastructure orchestration

  • OpenStack latest/stable release – VM orchestration
  • Kubernetes-based container orchestration
  • WeaveNet -based Container Networking
  • VNF Orchestration – ONAP
  • OS – Ubuntu 18.X LTS
  • CICD - Jenkins 2.249.1 LTS

SDN

ONOS will be used at the application layer


Workload type

VMs and Containers


Additional

  • 4 Virtual Machines
    1. One Orchestration node
    2. Three Edge nodes
  • Jenkins for Continuous Integration and Continuous Delivery
  • Personal servers will be integrated with the Linux Foundation servers

Contributors:

NameCompanyEmail (Contact)RolesProfilesJeju National Universitymalikasifmahmoodawan@gmail.comR&D, PTLsaqib haleemJeju National Universityem.saqb@gmail.comR&D@Muhammad Ali Jibran---alijibran35@gmail.comR&DJeju National Universityafaq24@gmail.comSupervisorWang-Cheol SongJeju National Universityphilo@jejunu.ac.krSupervisor
@Taekyung LeeATTO Researchtaekyung.lee@atto-research.comPartner

References:

#RelevanceTitle1IoV, PredictionAbbas, M.T., Muhammad, A. and Song, W.C., 2019. Road-aware estimation model for path duration in Internet of vehicles (IoV). Wireless Personal Communications, 109(2), pp.715-738.2IoV, PredictionAbbas, M.T., Jibran, M.A., Afaq, M. and Song, W.C., 2020. An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter. Transactions on Emerging Telecommunications Technologies, 31(5), p.e3734.3IoV, PredictionAbbas, M.T., Muhammad, A. and Song, W.C., 2020. SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach. Journal of Ambient Intelligence and Humanized Computing, 11(3), pp.1265-1280.4RSU, MECWang, X., Han, Y., Leung, V.C., Niyato, D., Yan, X. and Chen, X., 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), pp.869-904.5RSU, MECNdikumana, A., Tran, N.H., Kim, K.T. and Hong, C.S., 2020. Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing. IEEE Transactions on Intelligent Transportation Systems.

Tasks (for the team):

Task nameDetailsCreation
date
Completion
dateAssigneeStatusVoting for BPRequest and follow up voting process.

 

 

MalikAsif(tick)

Project Approved:

Request for PTLRequest to initiate the PTL.

 

 

(tick)

MalikAsifor saqib haleemgot  elected as PTL:

Setup PM
Setup the Physical Machine.

 

(warning)Setup JenkinsSetup Jenkins on PM.

 

(warning)Connect LF ServerIntegrate the Linux Foundation Servers.

 

(warning)Push CICD logsConfirmation of the CICD logs

 

 

(warning)Explore/Research K8s container protocol options in pythonExplore the containers inter/cross domain protocol options to enable communication among each other.

 

 

(warning)Explore/Research K8s network optionsExplore the networking plugin options that can be used in our proposed scenarios of vehicles. (Multus, etc)

 

 

(warning)Create documentation pages & subsections
  1. Architecture - figures/explanation
  2. Components - figures/explanation
  3. Scenarios - figures/explanation
  4. Implementation - plan/approach
  5. Ask Questions - toq

 

 

MalikAsif saqib haleem(warning)Create vehicle containers
  1. Vehicle Class
  2. Attributes
  3. Methods

 

 

MalikAsif(warning)CVB vehicle containersAsk CVB team, what analogy do they use to create vehicles/devices.

 

 

MalikAsif (warning)Maps
  1. Search ways for creating roads, intersection etc
  2. Implement roads. intersection etc

 

 

MalikAsif(warning)

Committers and PTL (Project Technical Lead)

Please enter in all names of the committers for the project.

PTL is done off of self nomination process.  If you wish to be considered for the PTL, please indicate that by putting a Y in the self nomination column (use the slide to move the table left to right).  Per Akraino rules, if there is only one nominee, that person becomes PTL (when confirmed by the Akraino TSC).  If there is more than nominee, we will then have an election.
The election process is open and will go through 7 Oct. 2020 at Noon Pacific time. 

Committer

Committer

Company

 Committer Contact InfoTime Zone

Committer Bio

Committer Picture

Self Nominate for PTL (Y/N)

Jeju National

Gachon University

malikasifmahmoodawan@gmail.com

Asia/Seoul (UTC+9)
Github Logo - Free social media iconsImage RemovedJeju National Universityem.saqb@gmail.comAsia/Seoul (UTC+9)

Motivation:

Motivational aspects are:

  • The topology in a vehicular network is updated and retrieved frequently
    • This causes path instability
  • Vehicular networks are wireless in nature
    • However, Software-defined networking (SDN) is originally designed for wired networks
  • Leads to the need for topology stability in vehicular networks

To this end, we introduce:

  • Computation at the Edge 
  • Topology prediction to proactively stabilize the paths in vehicular network
  • Proximity Services

Motivational IdeaImage Removed

Architecture Overview:

The architectural view of our system is as follows:

Image Removed

Use case Attributes:

  1. Stable network topology in IoV
  2. Road aware, predictive, and proactive connection

 1. Stable network topology in IoVImage Removed2. Road aware, predictive and proactive connectionImage Removed

Stable Path Scenarios:

Stable path connectivity scenarios are illustrated as shown below:

  • The below figure (shown left side) is a scenario where the devices are under same cell and in coverage, which want to communicate. The below figure (shown right side) is a scenario where the devices are under same cell and one oft them is out of coverage, which want to communicate. 

Image RemovedImage Removed

  • The below figure explores the Device to Device communication where a device 1 wants to communicate device-4 and both of them reside under a different Cell node. In this case, the packet is forwarded from one edge vEPC to another. This way the connectivity is provided as follows:

Image Removed

  • The below scenario explains the same as immediate previous scenario but the only difference among them is that one of the device is out of coverage and proximity service plays the role of provisioning the location of such devices.

Image Removed

  • The below figure shows the direct communication between the devices which reside under different Cells. The below path was provided as a result of proactive approach that we propose in our system. The major difference between the below two scenarios is that in such cases, we don't require the involvement of Edge vEPC in order to forward the packet to another road segment, thus reducing the latency and improving the performance in terms of bandwidth and efficient resource usage of the cellular spectrum. Both the devices are in coverage.

Image Removed

  • The below figure also shows the direct communication between the devices which reside under different Cells. The below path was provided as a result of proactive approach that we propose in our system. The difference from previously mentioned scenario is that one of these devices are out of coverage. In this case also, the benefits to reduce the latency and efficient resource usage were achieved.

Image Removed

First (1st)Second (2nd)

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to:

from:

to: Present


Gachon University


Asia/Seoul (UTC+9)

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