edge-to-cloud solutions - moodle.msengineering.ch

TSM-CloudSys

Edge-To-Cloud solutions

Nabil Abdennadher

T-CloudSys | Serverless / Function-as-a-Service | Academic year 2021/2022

The context (IoT today)• Thousands of connected IoT

sensors deployed at a large scale

• Tens (even hundreds) of applications coming, from different needs/domains/users, are emerging.

• Current IoT platforms tend to centralise intelligence in the cloud.

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T-CloudSys | Serverless / Function-as-a-Service | Academic year 2021/2022 3

The context: Centralised Cloud platforms

• AWS IoT Core• Azure IoT hub• Cloud IoT Core (Google)


The context: From Cloud to Edge-Cloud• For many IoT applications, submitted to constraints

the centralised vision is not sustainable.• Data production is exploding, making Cloud based

centralised IoT platforms, ill-equipped to cope withthe huge quantity of collected data.• The Centralised model is doomed to fail.• The need to make the edge smarter is

inescapable.• A new model, combining edge and cloud, is

required4


The context: A Hybrid Edge-Cloud platform

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PLAN

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• What is Edge Computing ?• Use-cases• Edge-Cloud solutions


What is Edge Computing ?

Edge computing is:• a method of optimising applications

by taking some “portions” away fromcentral nodes to the other extreme(the "edge").

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• a practice of processing data near the edge of the network,where the data is being generated, instead of processing in acentralised data-processing warehouse.


Why Edge Computing ?

• Four objectives are behind Edge computing:• Unload the cloud.• Limit the traffic between IoT devices and the

cloud• Keep decision as close as possible to the IoT

devices• Enhance security

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Why Edge Computing ?• Is there a need for near-real-time action on data

generated by sensors?• Is the data generated too big to transfer to the cloud?• Is the internet link between the sensors/actuators and the

cloud unreliable ?• Is there a privacy/security issue with transferring or

processing the data in the cloud (public or private)?

If the answer is yes to one or more of these questions, then you need an intelligent edge.

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https://media.sixsq.com/blog/case-for-edge-computing


Edge Computing markethttps://www.alliedmarketresearch.com/edge-computing-market

https://www.alliedmarketresearch.com/edge-computing-market


What are the Cons of Edge computing ?• Strike the balance between

• “keeping data at the edge” and “bringing it intoa central cloud”

• “sophisticated algorithms in the cloud” and“lightweight analytical processes” in the edge?

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Fog Computing

• Edge computing usually takes place directly on theedge device to which the sensors are attached.

• Fog computing moves the edge computingactivities to a set of edge devices that belong tothe same LAN

• In Fog Computing, computing activities arephysically more distant from the sensors andactuators.

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Self-adaptive ML based applications

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Closed/Feedback Loop


Self-adaptive ML based applications

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https://thenewstack.io/azure-iot-edge-a-technology-primer/

https://thenewstack.io/azure-iot-edge-a-technology-primer/


PLAN

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• What is Edge Computing ?• Use-cases• Edge-Cloud solutions


PrinciplePrinciple

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Low traffic

Medium traffic Medium light

Low light

High traffic Full light


Smart Lighting Solution - Edge application

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• Automatically recognise:• lanes, roundabouts, crossings

• Automatically set bounding boxes for traffic capture

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Smart public lighting

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Edge controlled Intelligence


Noise management

• Edge device: A noise “blind”detector based on acoustic and AItechnologies• 2 micros• Camera: only used for learning

• Classifying vehicles (cars, truck,buses, motorcycles, electric cars)

• Raspberry based prototype withpython software (NN), deployed atRue Jura (Geneva)

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From Monday Feb. 3 (5:00) to Friday Feb. (21:00): 13’783 vehicles

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Noise management


From Monday April. 6 (5:00) to Friday 10 April. (21:00): 7’178 vehicles

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Noise management


Noise management


• The intelligence is context-aware: 30 km zone, 1 lane, 2 lanes, 4 lanes, weather, etc.

• The “context” is related to spatial and temporal conditions.

• Remotely control theintelligence in case ofmisbehaving sensors orswitching from one contextto another (Continuousintegration)

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Noise management


Noise management


• The intelligence is context-aware: 30 km zone, 1 lane, 2 lanes, 4 lanes, weather, etc.

• The “context” is related to spatial and temporal conditions.

• Remotely control theintelligence in case ofmisbehaving sensors orswitching from one contextto another (Continuousintegration)

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Noise management


Edge controlled Intelligence

Noise management


Data integrity• IoT applications often use low quality and/or non institutional

sensors• Low energy sensors: Most of IoT devices are battery powered

and should operate in an energy saving way.• Limited computing resources: minimal processing and

storage capability• Low cost/quality sensors: To keep the prices low, vendors

often use low quality hardware.• Not secure: Not build with security in mind

• à IT security techniques cannot be used

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And here is where trust comes in …


Data integrity

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Data integrity

~550 LoRa noise sensors

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Data integrity

• Signature

• Noisy streets, residential neighborhoods, 30 kmzones, etc.

• Precision/Accuracy

• Difference between the measure we receive andthe value a good quality sensor would give.

• Packet Error Rate

• Availability32


Use-case 4: Signature Trust Factor

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Smart Grid

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Power Grid


Smart Grid

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Micro-grid infrastructure Power Grid

Micro-grid infrastructure


Smart Grid

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Connection to the power Grid


Smart Grid• Digital self-adaptive smart grid platform:

• An IoT-Edge-Cloud infrastructure composed of:• Home appliances & smart meters• Edge devices (yellow, blue and green boxes)• Cloud infrastructure

• A ML based self-adaptive application

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Initial data Learning 1

New deployment

Failure HouseholdLearning N

.

.

.

Prediction 1

Prediction N

.

.

.


Smart Grid

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PLAN

• What is Edge Computing ?• Use-cases• Edge-To-Cloud solutions

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Deploying on the edge-to-cloud platforms


Anatomy of Edge-To-Cloud solutions

• IoT Infrastructure Management: An orchestration service thatcomposes, provisions (configures and deploys) and monitorsEdge/IoT networks.

• Edge Framework: enable edge application modules programmingand execution.

• Container Facilities: build a container (such as Docker) with atrained MLM

• Communication Hub: create a messaging infrastructure fornetworked components

• Storage Facilities: store training data and several MLM versions inCloud data warehouses.

• Machine Learning Facilities: build and train a MLM in the Cloud.

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edge-to-cloud solutions - moodle.msengineering.ch

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