Common Data and Intelligence Layer

Edgar Ramos (Ericsson)Roberto Morabito (Princeton U.)Marie-José Montpetit ( U. Concordia)Eve Schooler ( Intel)

April 2020

Centralized vs. Distributed Computing Architectures

From centralized to

distributed

Data sources

Execution environment

Applications

Centralized Decentralized Distributed

Level of interoperability dependency

— g Inference Execution DistributionAI Service Functional Distribution

Intelligence provisioning (Model distribution)

Agent Functional Distribution

Training Execution Distribution

Computational Intelligence Distribution Aspects

Swarm

HierarchicalShape analyser

Colour analyser

Fruit Recognition

Edge-Computing Fabric Stacks Mapping

Knowledge

Data

Intend &purpose

Sem

antic

s

Communication

Sens

ing

&

Actu

atio

n ca

pabi

litie

s

AgentInteraction

HW/SW Platform

Orc

hest

ratio

n Local Data Sources/Repository

Local Processing

Common Data/Intelligence Layer

Orc

hest

ratio

n

Services & Functions

Peering

Policies Handling

Services Exposure

LCM

Applications

What to do or what does it need from others and how is configured or directed about what to do (or orchestrated)

General and domain specific vocabularies and ontologies that can interoperate

Exchange of processed information according to interaction

(filtering, searching, contextualizing, negotiating, etc.)

Measurement, reports, identifiers, digital representations, etc. Considerations such as data nomadism

(data moving with physical entities)

Multiple topologies and communication modes(D2D, D2Nw, Nw2Nw)

Interaction and negotiation process( cooperate, compete, persuade, block)

Edge architecture stack Functional stack

Monitoring

• Environmental

• Climate

• Irrigation/

nutriment

Local image

processing/sensor

fusion

IoT broker

Local databases

Local decision

Formatting

Match/action

Query/response

Store/process

Data Communications

Data CaptureEdge Computing Cloud Computing

LAN• WSN

• WIFI

• Bluetooth

• Zigbee

• LORA

• RFID

Sensors

• Temperature (

• Humidity

• Moisture

• Ph

• CO2

Cameras

• IR

• Visual

Operator(s)

Farm management

Application/API

management

IoT Broker

Event databases/logs

Cloud-based image

processing/training

Datasets

Remote decision

Status and diagnostics

Cyberphysical security

PLC/Automation

Middleware

Application services

• Phone

• PC/Mac

• Tablet

Local ML/AI

Operator(s)

Alerts/critical

events

Data Interpretation

Ale

rts/

crit

ica

l

eve

nts

CADM

IP Protocols⁃ UDP

• TCP

• HTTPS

• QUIC IoT

• IoT specific

Data Formats & semantics⁃OneDM

•SENML

•LWM2M

•WoT

•JSON-LD

WAN•Cellular

•Fiber +WIFI

•HFC+WIFI

•xDSL+WIFI

Com

mon

Dat

a La

yer i

n an

Ap

plic

atio

n co

ntex

t

Summary

• Interoperability requirements analysis (according to different scenarios)• Common Data Layer taxonomy• "Common Data Layer for COIN" draft

• Possible legacy with the "Edge Data Discovery for COIN" draft

Additional References:Distributing Intelligence to the Edge and BeyondIntelligence Stratum for IoT. Architecture Requirements and Functions

• One of the main challenges in computational intelligence is the lack of interoperability between AI components• Such issue is clearly accentuated at the data layer (e.g., incompatible data

types and formats, heterogeneous APIs and platforms required to execute the AI models, inconsistent life-cycle management and policies)• A Common Intelligence-Data Layer is needed.

Next Steps