Hack2023 1st Prize - Revision history

Velez at 09:04, 28 March 2024

2024-03-28T09:04:39Z

← Older revision		Revision as of 10:04, 28 March 2024
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			<p class="left" style="font-size:26px;"> ←''[[ETSI_-_LF_-_OCP_-_MEC_Hackathon_2023\|MEC Hackathon 2023]]''<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>}}

Velez at 13:36, 11 December 2023

2023-12-11T13:36:45Z

← Older revision		Revision as of 14:36, 11 December 2023
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	= 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 presentation pitch at the Edge Computing World conference [[https://wiki.akraino.org/download/attachments/61835204/ETSI-LFE-HACKATHON-DOMINO.mp4 here]]

	~~-->~~
			{{#evu:https://www.youtube.com/watch?v=zmEYpDqo0FU
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			}}

Velez at 13:26, 11 December 2023

2023-12-11T13:26:00Z

Velez at 11:46, 6 December 2023

2023-12-06T11:46:21Z

Show changes

Velez at 11:19, 6 December 2023

2023-12-06T11:19:32Z

← Older revision		Revision as of 12:19, 6 December 2023
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	<div class="panel panel-default">		<div class="panel panel-default">
	<div class="panel-body">		<div class="panel-body">
	Team from '''Optare Solutions'''		Team '''Green Cyclops''' from '''Optare Solutions'''
	* Xose Ramon Sousa Vazquez		* Xose Ramon Sousa Vazquez
	* Santiago Rodriguez Garcia		* Santiago Rodriguez Garcia

Velez: Created page with "{{DISPLAYTITLE:{{FULLPAGENAME}}}}
1^st Prize Award

Managing natural resources using AI, Edge Computing and Advanced Communication

= Team=

2023-12-06T11:16:44Z

Created page with "{{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>1<sup>st</sup> Prize Award</b><p> <p class="center" style="font-size:34px;"><b> Managing natural resources using AI, Edge Computing and Advanced Communication </b><p> <br> = Team= <div class="flex-row row"> <div class="col-xs-5 "> <div class="panel panel-default"> <div class="panel-bo..."

New page
{{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>1<sup>st</sup> Prize Award</b><p>

<p class="center" style="font-size:34px;"><b> Managing natural resources using AI, Edge Computing and Advanced Communication

</b><p>

<br>

= Team=

<div class="flex-row row">

<div class="col-xs-5 ">
<div class="panel panel-default">
<div class="panel-body">
Team from '''Optare Solutions'''
* Xose Ramon Sousa Vazquez
* Santiago Rodriguez Garcia
* Fernando Lamela Nieto

</div>


</div>
</div>

<div class="col-xs-7">
<div class="panel panel-default">

<div class="panel-body">
[[File:Edge Hackathon 2022 - Automotive Special Prize.jpg|600px|center|top|class=img-responsive]]
</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>
</div>

</div>

= Introduction =
<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 platform with this information which can predict and adopt several actions for the good of people.

[[File:hack2022-optare-architecture1v2.png|800px|center|top|class=img-responsive]]
<br>
* 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.
</p>

= Architecture =
<br>
[[File:hack2022-optare-architecture2.png|800px|center|top|class=img-responsive]]
<br><br>
[[File:hack2022-optare-architecture3.png|800px|center|top|class=img-responsive]]
<br><br>

= Use Cases =

== UC1: Historical Data collection from autonomous MMTC Edge ==
[[File:hack2022-optare-component-UC1.png|800px|center|top|class=img-responsive]]

* 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]]

* 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>
= Software resources =

• '''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"
|+
|-
|'''Innovation'''
|[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|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'''
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|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'''
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|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'''
||[[File:hack2022-optare-outcome2.png|60px|center|class=img-responsive]]
|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'''
||[[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>


= Project Videos =
SOON

← Older revision		Revision as of 14:26, 11 December 2023
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	A computer vision platform or ecosystem that utilizes 5G, edge computing, and AI to handle		A computer vision platform or ecosystem that utilizes 5G, edge computing, and AI to handle
	multiple video stream sources from various devices such as drones, fixed cameras, and IoT		multiple video stream sources from various devices such as drones, fixed cameras, and IoT
	sensors. This platform aims to enable the application of different AI detection and analytic		sensors. </p>
	models based on user selection. The inference process takes place at the edge, where		<p>
	computational resources are shared to execute AI models relevant to different scenarios,		This platform aims to enable the application of different AI detection and analytic
	including forest fire and smoke detection, wildlife control, and deforestation.		models based on user selection. </p>
	~~The key components and functionalities of this computer vision platform can be described as~~		<p>
	~~follows:~~		The inference process takes place at the edge, where computational resources are shared to execute AI models relevant to different scenarios, including forest fire and smoke detection, wildlife control, and deforestation.
			</p>


	[[File:Green-cyclops.png\|800px\|center\|top\|class=img-responsive]]		[[File:Green-cyclops.png\|800px\|center\|top\|class=img-responsive]]
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	The key components and functionalities of this computer vision platform can be described as		The key components and functionalities of this computer vision platform can be described as
	follows:		follows:
	* Video Stream Management: The platform receives and manages multiple video		<p>
			• Video Stream Management: The platform receives and manages multiple video
	streams and information from diverse sources, such as drones, fixed cameras, and IoT		streams and information from diverse sources, such as drones, fixed cameras, and IoT
	sensors. These streams are transmitted via the high-speed and low-latency capabilities		sensors. These streams are transmitted via the high-speed and low-latency capabilities
	of a 5G network.		of a 5G network.
			</p>
	* IoT Events Management: The platform receives and manages multiple events from		<p>
			• IoT Events Management: The platform receives and manages multiple events from
	devices placed on the ground to add information to the use cases in the described		devices placed on the ground to add information to the use cases in the described
	scenario.		scenario. </p>
			<p>
	* Edge Computing: The platform leverages edge computing infrastructure, which brings		• Edge Computing: The platform leverages edge computing infrastructure, which brings
	computational resources closer to the video sources. This allows for real-time analysis		computational resources closer to the video sources. This allows for real-time analysis
	and decision-making at the network edge, reducing the need for data transmission to a		and decision-making at the network edge, reducing the need for data transmission to a
	central server or cloud. Edge computing facilitates faster response times and more		central server or cloud. Edge computing facilitates faster response times and more
	efficient resource utilization.		efficient resource utilization.</p>
			<p>
	* AI Detection and Analytic Models: The platform supports the selection and deployment		• AI Detection and Analytic Models: The platform supports the selection and deployment
	of different AI detection and analytic models based on specific requirements. For		of different AI detection and analytic models based on specific requirements. For
	example, models can be chosen for forest fire and smoke detection, wildlife monitoring,		example, models can be chosen for forest fire and smoke detection, wildlife monitoring,
	or deforestation analysis. These models utilize computer vision techniques, such as		or deforestation analysis. These models utilize computer vision techniques, such as
	object detection, image classification, and semantic segmentation, to extract		object detection, image classification, and semantic segmentation, to extract
	meaningful information from the video streams.		meaningful information from the video streams.</p>
			<p>
	* Model Inference at the Edge: The selected AI models are executed at the edge		• Model Inference at the Edge: The selected AI models are executed at the edge
	computing nodes, where the necessary computational resources are available. This		computing nodes, where the necessary computational resources are available. This
	eliminates the need to transmit large volumes of video data to a centralized server or		eliminates the need to transmit large volumes of video data to a centralized server or
	cloud for inference. By performing inference at the edge, the platform achieves		cloud for inference. By performing inference at the edge, the platform achieves
	real-time analysis and enables quick response to detected events or anomalies.		real-time analysis and enables quick response to detected events or anomalies.</p>
			<p>
	* Resource Sharing: allowing for the simultaneous execution of multiple AI models on		• Resource Sharing: allowing for the simultaneous execution of multiple AI models on
	different video streams. This flexibility enables efficient resource allocation and		different video streams. This flexibility enables efficient resource allocation and
	scalability to handle varying workloads.		scalability to handle varying workloads.</p>
			<p>
	* Scenario-Specific Applications: The platform is adaptable and customizable to address		• Scenario-Specific Applications: The platform is adaptable and customizable to address
	a wide range of use cases.		a wide range of use cases.</p>
			<p>
	* Cloud Management Applications: The platform delivers cloud components for high level		• Cloud Management Applications: The platform delivers cloud components for high level
	supervision and management purposes, as well as use case configuration to be		supervision and management purposes, as well as use case configuration to be
	deployed over the edge nodes.		deployed over the edge nodes.</p>
			<p>
	* Sustainable applications: All the AI applications will be measured in terms of energy		• Sustainable applications: All the AI applications will be measured in terms of energy
	consumption making smart decisions on how to run trying to make a sustainable		consumption making smart decisions on how to run trying to make a sustainable
	context		context</p>
			<p>
	* The offloading of the AI to the edge makes more efficient the drone consumption		• The offloading of the AI to the edge makes more efficient the drone consumption
	helping in the autonomy of the UAVs		helping in the autonomy of the UAVs