Hack2022 3rd Prize: Difference between revisions

From MECwiki
Jump to: navigation, search
No edit summary
No edit summary
 
(19 intermediate revisions by the same user not shown)
Line 1: Line 1:
<p class="left" style="font-size:26px;"> ←''[[ETSI_-_LF_MEC_Hackathon_2022|MEC Hackathon 2022]]''<p>
<br>
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}
{{DISPLAYTITLE:<span style="position: absolute; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px);">{{FULLPAGENAME}}</span>}}


<p class="center" style="font-size:34px;"><b>3<sup>rd</sup> Prize Award & Automotive special prize</b><p>
<p class="center" style="font-size:34px;"><b>3<sup>rd</sup> Prize Award & Automotive Special Prize</b><p>


<p class="center" style="font-size:34px;"><b> K.I.T.T Knowledge in the traffic
<p class="center" style="font-size:34px;"><b> K.I.T.T Knowledge in the traffic
Line 12: Line 14:
<div class="flex-row row">
<div class="flex-row row">


<div class="col-xs-12 col-md-6 col-lg-7">
<div class="col-xs-5 ">
<div class="panel panel-default">
<div class="panel panel-default">
<div class="panel-body">
<div class="panel-body">
Team '''Pedraforca''' from '''Optare Solutions'''
Team from '''Optare Solutions'''
* Xose Ramon Sousa Vazquez
* Xose Ramon Sousa Vazquez
* Santiago Rodriguez Garcia  
* Santiago Rodriguez Garcia  
Line 26: Line 28:
</div><!-- End of col 1-->
</div><!-- End of col 1-->


<div class="col-xs-12 col-md-6 col-lg-5">
<div class="col-xs-7">
<div class="panel panel-default">
<div class="panel panel-default">


Line 32: Line 34:
[[File:Edge Hackathon 2022 - Automotive Special Prize.jpg|600px|center|top|class=img-responsive]]
[[File:Edge Hackathon 2022 - Automotive Special Prize.jpg|600px|center|top|class=img-responsive]]
</div>
</div>
  <div class="panel-footer">Santiago Rodriguez & Fernando Lamela with Jury members Bob Gazda (InterDigital) and Jyoti Sharma (5GAA Board/Verizon)</div>  
  <div class="panel-footer">From left to right,  Fernando Lamela and Santiago Rodriguez with Jury members Bob Gazda (InterDigital) and Jyoti Sharma (5GAA Board/Verizon)</div>  
<!--  <div class="panel-footer">Footer text 1</div>  -->
<!--  <div class="panel-footer">Footer text 1</div>  -->
</div><!-- End of pan -->
</div><!-- End of pan -->
Line 41: Line 43:
= Introduction =  
= Introduction =  
<p>
<p>
* A connected vehicle (car) that with the support of 5G, MEC and artificial intelligence technologies is able to capture information from the surrounding environment and feed a smart city platform with this information which can predict and adopt several actions for the good of people.
* A connected vehicle (car) that with the support of 5G, MEC and artificial intelligence technologies is able to capture information from the surrounding environment and feed a smart platform with this information which can predict and adopt several actions for the good of people.


[[File:hack2022-optare-architecture1.png|800px|center|top|class=img-responsive]]
[[File:hack2022-optare-architecture1v2.png|800px|center|top|class=img-responsive]]
<br>
<br>
* The information retrieved by the car and sent to the platform is collected by three different ways:
* The information retrieved by the car and sent to the platform is collected by three different ways:
<ul>
** '''camera:''' the car is equipped with a camera that sends a video stream to the edge over which is executed an AI inference algorithm to detect several patterns and generate information packages relevant to the smart platform.
<li>camera: the car is equipped with a camera that sends a video stream to the edge over which is executed an AI inference algorithm to detect several patterns and generate information packages relevant to the smart city platform</li>
** '''sensors:''' the car is equipped with several sensors (temperature, humidity, …) that can create heat maps with this information to the smart platform.
** '''the smart environment itself'''. In regions/areas where the information sources are isolated (low power, low signal quality, bad coverage due maintenance, weather conditions, etc), the car can act like a link between the information source and the smart environment platform, uploading this information in the next available coverage area crossed by the car in its route.


<li>sensors: the car is equipped with several sensors (temperature, humidity, …) that can create heat maps with this information to the smart city </li>
* Every information contribution to the smart environment could be rewarded with points that can be exchanged with tax discounts, for example, guaranteeing the participation of the people establishing missions with a different degree of value.


<li>the smart city itself. In regions/areas where the city information sources are isolated (low power, low signal quality, bad coverage due maintenance, weather conditions, etc), the car can act like a link between the information source and the smart city platform, uploading this information in the next available coverage area crossed by the car in its route.</li>
* Every connected car can register on the smart environment platform and provide several information about the environment and people.  
</ul>
* Every information contribution to the smart city could be rewarded with points that can be exchanged with tax discounts, for example, guaranteeing the participation of the people establishing missions with a different degree of value. Every connected car can register on the smart city platform and provide several information about the city and people.  
</p>
</p>


Line 64: Line 65:
<br><br>
<br><br>


= Component view =
= Use Cases =
 
== UC1: Historical Data collection from autonomous MMTC Edge ==  
[[File:hack2022-optare-component-UC1.png|800px|center|top|class=img-responsive]]
[[File:hack2022-optare-component-UC1.png|800px|center|top|class=img-responsive]]
<br>
 
* Context: two different networks,
** Provided by Operator 1, connected, with Edge services in different nodes.
** Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.
 
'''UC.1:''' a connected car scenario where it can act, once registered, as a carrier of the historical data generated by all the IoT devices connected to the MMTC Edge during an interval of time, in order to be stored and processed by services and the resources provided by the Operator 1 Edge.
 
* Considerations:
** MMTC Edge need to be efficient and guaranty IoT Flows based services to local verticals, with limited resources and delivers local 5G connectivity to IoT Devices
** Edge can have more resources, in addition to 5G connectivity, with backhaul, and can serve applications and services where it can balance between local and cloud.
<br><br>
== UC2: Service APP Update to autonomous MMTC Edge ==
<br>  
[[File:hack2022-optare-component-UC2.png|800px|center|top|class=img-responsive]]
[[File:hack2022-optare-component-UC2.png|800px|center|top|class=img-responsive]]
* In the context of two different networks,
** Provided by Operator 1, connected, with Edge services in different nodes.
** Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.
'''UC.2:''' a connected car scenario where it can act, once registered, as a carrier of new versions, if exist, of MMTC IoT Security Flows, as a part of the ‘firmware’ of the node, that can be updated once security checks were passed.
* Considerations:
** MMTC Edge provides different functionalities related with the management of IoT devices and data produced, delivering an optimized environment for some verticals in locations with reduced access to broadband communications.
** MMTC Edge provides secure graphical access for customers to modify their own IoT Flows, but no to update or modify security flows (firmware), that only can be updated by an operator or by a connected car (with this UC) once is registered and security checks are made.
<br><br>
<br><br>
= Conclusion =
= Software resources =
<br>
 
• '''Project repository'''
 
https://github.com/flamela/kitt-etsi-hackathon22
[[File:hack2022-optare-car_dashboard.png|600px|center|class=img-responsive]]
 


= Hackathon Alignment =
<p> This project has fullfilled the hackathon criteria required by the jury </p>
{| class="wikitable"
{| class="wikitable"
|+
|+
!Name of the project
!Description
!MEC Components provided
!MEC APIs supported
!Link
!Contact
|-
|-
|'''AdvantEdge''' [[File:advantedge.png|center|frameless|200x200px]]
|'''Innovation'''  
|AdvantEDGE is a Mobile Edge Emulation Platform (MEEP) that runs on Docker & Kubernetes. AdvantEDGE provides an emulation environment, enabling experimentation with Edge Computing Technologies, Applications, and Services. The platform facilitates exploring edge / fog deployment models and their impact on applications and services in short and agile iterations.
|[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|MEC Platform
|Digital divide of 5g end edge in some areas because its complexity and the
| MEC 012 Radio Network Information<br />MEC 013 Location<br />MEC 028 WLAN Information
 
|[https://github.com/InterDigitalInc/AdvantEDGE Link]
cost to bring. The mobile backhaul via vehicular net could help on that
|[mailto:AdvantEDGE@InterDigital.com AdvantEDGE]
 
proposing a network and computation for agriculture and remote industries
|-
|-
|'''Connected Vehicle Blueprint (Aka CVB)''' [[File:akraino.png|center|frameless|200x200px]]
|'''Use-Case / Solution Credibility'''  
|CVB provides a V2X focused MEC platform, which offers services to connected vehicles. These services are delivered to applications hosted on vehicles based on a set of policies for data dispatch and response. As the blueprint continues to be developed, further connected-vehicle applications and services are being incorporated into the blueprint.
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|MEC Platform(s), MEC Platform Manager
|Create a platform with capability for sending and receive data using the
|MEC 011 Mp1 & Mm5
 
|[https://wiki.akraino.org/pages/viewpage.action?pageId=9601601 Link]
different edge covered areas and using cheap and autonomous equipment
|[mailto:yargyang@tencent.com Yarg Yang]
 
for a quick deployment. Integrated demo is created for hackathon
|-
|-
|'''Eclipse fog05''' [[File:Fog5.png|center|frameless|150x150px]]
|'''Use of ETSI MEC Services'''  
|Eclipse fog05 provides a decentralised infrastructure for provisioning and managing compute, storage, communication and I/O resources available anywhere across the network. Eclipse fog05 addresses highly heterogeneous systems even those with extremely resource-constrained nodes. Eclipse fog05 can be used to deploy MEC Applications on centralised or distributed MEC Platforms.
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|MEC Orchestrator
|MEC013 for location information, MEC012 for radio network information,
|MEC 010-2 Application descriptor information model
 
|[https://fog05.io/ Link]
MEC021 for mobility and depending on the availability of more than one
|[https://gitter.im/atolab/fog05 Gitter community channel]
 
sandbox mep, MEC030 related with V2X and the automotive blueprint
 
MEC011 for application registration, mec service discovery
|-
|-
|'''Eclipse zenoh''' [[File:zenoh.png|center|frameless|150x150px]]
|'''Use of LF Edge Akraino Blueprints'''  
|Eclipse zenoh unifies data in motion, data in-use, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks. It is a perfect fit as an alternative transport protocol for MEC applications as well as technological stack to build distributed MEC platforms.
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|Alternative transport protocol for MEC Platform
|Checked the Elliot Manager and Nodes. Needs a continuum connectivity
|MEC 011 Mp1
 
|[http://zenoh.io/ Link]
among the components and this is not possible in this architecture. Follow
|[https://gitter.im/atolab/zenoh Gitter community channel]
 
|-
some APIs for upload new app images. Tested MEC-based Stable Topology
|'''EdgeGallery''' [[File:Edgegallery.png|center|frameless|200x200px]]
 
|The EdgeGallery community focuses on providing a MEC platform framework at a carrier’s network edge, whilst adopting the de facto standard of network service openness through open source collaboration. The framework enables MEC edge resources and applications, whilst providing both security and management capabilities. It also offers interconnectivity with the public cloud. The result is the creation of unified MEC application ecosystem, which is designed to be compatible across a carrier’s heterogeneous edge infrastructure.
Prediction for Vehicular Networks but similar and more evolved with MEC030
|MEC Open Source Platform
|MEC 010-2，MEC 011，MEC 009，and will support more ETSI APIs in further releases
|[https://www.edgegallery.org/en/ Link]
|[https://edgegallery.groups.io/g/main Maillist]
|-
|'''Enterprise Applications on Lightweight 5G Telco Edge (EALTEdge)''' [[File:akraino.png|center|frameless|200x200px]]
|Lightweight telco edge platform, enabling Enterprise applications on telco edge. Offering a: Unified Portal for platform management and for App developers; Sandbox with SDKs and tools chains for MEC app developers; Heterogeneous deployment on Multi-Arch; ETSI MEC Compliance.
|MEC Platform(s), MEC Platform Manager
|MEC 011 Mp1 & Mm3
|[https://wiki.akraino.org/display/AK/Enterprise+Applications+on+Lightweight+5G+Telco+Edge Link]
|[Mailto:gaurav.agrawal@huawei.com Gaurav Agrawal]
|-
|'''i-MEC''' [[File:Italtel RGB.jpg|center|frameless|200x200px]]
|Italtel MEC platform i-MEC brings high value in the network enabling a wide set of services which leverage reduced end-to-end latency (uRLLC), pre-processing at the edge (mMTC) and broadband services (eMBB). i-MEC contributes to reduce the traffic load on the backhauling transport network with relevant saving of cost for the Service Operator.
|MEC Platform
|MEC011 Mp1, Mm5 proprietary API, Mp2 proprietary API (OpenFlow based)
|[https://www.italtel.com/products/multi-access-edge-computing-mec-platform/ Link]
|[Mailto:marketing_technology@italtel.com Italtel]
|-
|'''LightEdge''' [[File:lightedge-logo.png|center|frameless|150x150px]]
|LightEdge is a lightweight, ETSI-compliant MEC solution for 4G and 5G networks. It is designed to work natively on top of Kubernetes and is transparent to the existing components of a 4G network, therefore requiring zero modifications to the MNO’s environment.
| MEC Platform
| RNI (MEC-012), WIA (MEC-028), partially Application Enablement (MEC-011)
|[http://lightedge.github.io/ Link]
|[mailto:roberto.riggio@gmail.com Roberto Riggio]
|-
|'''MEC Location API Simulator''' [[File:LINKS_Logo.png|center|frameless|200x200px]]
|The Location API simulator helps developers to create applications that use MEC Location API. It provides a MEC Location Service accessible via Location API as specified in the ETSI GS MEC013 document, available as a RESTful web service. It has a Graphical User Interface enabling developers to simulate mobile users' movements by feeding the simulator with a GPS track in .gpx format. The first release implements a subset of MEC013 API but the full set of APIs and an improved engine to simulate cars, VRU and more will be part of future releases.
| MEC013 accessible APIs (with an engine to simulate mobile users’ movement)
| MEC 013 Location
|[https://networkbuilders.intel.com/commercial-applications/links-foundation Link]
|[mailto:daniele.brevi@linksfoundation.com Daniele Brevi]
|-
|'''Public Cloud Edge Interface (PCEI)''' [[File:akraino.png|center|frameless|200x200px]]
|The purpose of Public Cloud Edge Interface (PCEI) Blueprint family is to specify a set of open APIs for enabling Multi-Domain Inter-working across  functional domains that provide Edge capabilities/applications and require close cooperation between the Mobile Edge, the Public Cloud Core and Edge, the 3rd-Party Edge functions as well as the underlying infrastructure such as Data Centers and Networks.
|Provides an enabler layer that facilitates interworking between Edge Computing platforms, including Multi-Access Edge Compute, Public Cloud and 3rd-Party Edge Compute, and Mobile Networks
|MEC 013 Location API
|[https://wiki.akraino.org/display/AK/Public+Cloud+Edge+Interface+%28PCEI%29+Blueprint+Family Link]
|[mailto:oberzin@equinix.com Oleg Berzin]
|-
|'''ServerlessOnEdge'''[[File:Logo-small.png|center|frameless|200x200px]]
|Decentralized framework for the distribution of lambda functions to multiple serverless platforms, with Apache OpenWhisk connectors, supporting the ETSI MEC Device application interface (MEC 016).
|User app LCM proxy
|MEC 016 Device application interface (Mx2)
|[https://github.com/ccicconetti/serverlessonedge Link]
|[Mailto:c.cicconetti@iit.cnr.it Claudio Cicconetti]
|-
|'''Simu5G'''[[File:Simu5G logo.png|center|frameless|200x200px]]
|This open-source framework integrates an ETSI-compliant implementation of the MEC system within Simu5G, a simulator for the data plane of 5G networks. Communications with the MEC platform and the User App LCM proxy employ RESTful APIs compliant with the reference points standardized by ETSI. MEC services gather network-related information (e.g. UE location, RNI) from the simulated underlying 5G network. Moreover, thanks to the ETSI-compliant APIs and the fact that Simu5G runs as a real-time emulator, one can test real MEC apps in real time in controllable scenarios.
|User App LCM proxy, MEC platform
|MEC 011 Mp1, MEC 016 Mx2, MEC 012 RNI (L2meas), MEC 013 Location (UE/gNB location, circleNotificationSubscription)
|[http://simu5g.org/MEC.html Link]
|[Mailto:giovanni.nardini@unipi.it Giovanni Nardini, University of Pisa ]
|-
|-
|'''Deliverable'''
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|Developed all the sw components running in a demo environment. Designed
the autonomous antenna (Spanish self delivery frequency and edge
platform). Tested with a wifi connection and a static 5G antenna
|-
|-
|}
|}
<br>
<br>
[[File:hack2022-optare-outcome.png|800px|center|top|class=img-responsive]]
<!--
<!--
* Full details [//mecwiki.etsi.org/images/MEC_Service_Federation_for_Location-aware_IoT_with_DevOps_MEC_Infra_Orchestration_v3.pdf here]
* Full details [//mecwiki.etsi.org/images/MEC_Service_Federation_for_Location-aware_IoT_with_DevOps_MEC_Infra_Orchestration_v3.pdf here]
Line 177: Line 162:


= Project Videos =  
= Project Videos =  
SOON
<!--  
<!--  
* See the demo video of the project [[https://wiki.akraino.org/display/AK/ETSI-LF+Edge+Akraino+Hackathon+2022?preview=/61835204/61835620/DOMINO-DEMO-EQIX-5G-MEC.mp4 here] ]
* See the demo video of the project [[https://wiki.akraino.org/display/AK/ETSI-LF+Edge+Akraino+Hackathon+2022?preview=/61835204/61835620/DOMINO-DEMO-EQIX-5G-MEC.mp4 here] ]

Latest revision as of 10:10, 28 March 2024

←MEC Hackathon 2022


3rd Prize Award & Automotive Special Prize

K.I.T.T Knowledge in the traffic


Team

Team from Optare Solutions

  • Xose Ramon Sousa Vazquez
  • Santiago Rodriguez Garcia
  • Fernando Lamela Nieto
Edge Hackathon 2022 - Automotive Special Prize.jpg
From left to right, Fernando Lamela and Santiago Rodriguez with Jury members Bob Gazda (InterDigital) and Jyoti Sharma (5GAA Board/Verizon)

Introduction

  • A connected vehicle (car) that with the support of 5G, MEC and artificial intelligence technologies is able to capture information from the surrounding environment and feed a smart platform with this information which can predict and adopt several actions for the good of people.
Hack2022-optare-architecture1v2.png


  • The information retrieved by the car and sent to the platform is collected by three different ways:
    • camera: the car is equipped with a camera that sends a video stream to the edge over which is executed an AI inference algorithm to detect several patterns and generate information packages relevant to the smart platform.
    • sensors: the car is equipped with several sensors (temperature, humidity, …) that can create heat maps with this information to the smart platform.
    • the smart environment itself. In regions/areas where the information sources are isolated (low power, low signal quality, bad coverage due maintenance, weather conditions, etc), the car can act like a link between the information source and the smart environment platform, uploading this information in the next available coverage area crossed by the car in its route.
  • Every information contribution to the smart environment could be rewarded with points that can be exchanged with tax discounts, for example, guaranteeing the participation of the people establishing missions with a different degree of value.
  • Every connected car can register on the smart environment platform and provide several information about the environment and people.


Architecture


Hack2022-optare-architecture2.png



Hack2022-optare-architecture3.png



Use Cases

UC1: Historical Data collection from autonomous MMTC Edge

Hack2022-optare-component-UC1.png
  • Context: two different networks,
    • Provided by Operator 1, connected, with Edge services in different nodes.
    • Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.

UC.1: a connected car scenario where it can act, once registered, as a carrier of the historical data generated by all the IoT devices connected to the MMTC Edge during an interval of time, in order to be stored and processed by services and the resources provided by the Operator 1 Edge.

  • Considerations:
    • MMTC Edge need to be efficient and guaranty IoT Flows based services to local verticals, with limited resources and delivers local 5G connectivity to IoT Devices
    • Edge can have more resources, in addition to 5G connectivity, with backhaul, and can serve applications and services where it can balance between local and cloud.



UC2: Service APP Update to autonomous MMTC Edge


Hack2022-optare-component-UC2.png
  • In the context of two different networks,
    • Provided by Operator 1, connected, with Edge services in different nodes.
    • Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.

UC.2: a connected car scenario where it can act, once registered, as a carrier of new versions, if exist, of MMTC IoT Security Flows, as a part of the ‘firmware’ of the node, that can be updated once security checks were passed.

  • Considerations:
    • MMTC Edge provides different functionalities related with the management of IoT devices and data produced, delivering an optimized environment for some verticals in locations with reduced access to broadband communications.
    • MMTC Edge provides secure graphical access for customers to modify their own IoT Flows, but no to update or modify security flows (firmware), that only can be updated by an operator or by a connected car (with this UC) once is registered and security checks are made.



Software resources

• Project repository

https://github.com/flamela/kitt-etsi-hackathon22

Hack2022-optare-car dashboard.png


Hackathon Alignment

This project has fullfilled the hackathon criteria required by the jury

Innovation
Hack2022-optare-outcome2.png
 Digital divide of 5g end edge in some areas because its complexity and the

cost to bring. The mobile backhaul via vehicular net could help on that

proposing a network and computation for agriculture and remote industries

Use-Case / Solution Credibility
Hack2022-optare-outcome2.png
 Create a platform with capability for sending and receive data using the

different edge covered areas and using cheap and autonomous equipment

for a quick deployment. Integrated demo is created for hackathon

Use of ETSI MEC Services
Hack2022-optare-outcome2.png
 MEC013 for location information, MEC012 for radio network information,

MEC021 for mobility and depending on the availability of more than one

sandbox mep, MEC030 related with V2X and the automotive blueprint

MEC011 for application registration, mec service discovery

Use of LF Edge Akraino Blueprints
Hack2022-optare-outcome2.png
 Checked the Elliot Manager and Nodes. Needs a continuum connectivity

among the components and this is not possible in this architecture. Follow

some APIs for upload new app images. Tested MEC-based Stable Topology

Prediction for Vehicular Networks but similar and more evolved with MEC030

Deliverable
Hack2022-optare-outcome2.png
 Developed all the sw components running in a demo environment. Designed

the autonomous antenna (Spanish self delivery frequency and edge

platform). Tested with a wifi connection and a static 5G antenna


Project Videos

SOON

Retrieved from "https://mecwiki.etsi.org/index.php?title=Hack2022_3rd_Prize&oldid=4235"