FogFlow enabled Data Sharing across LoRa ...

FogFlow enabled Data Sharing across LoRa Applications Bin Cheng (NEC Labs Europe) Sylvain Prost (The Things Industrie)

LoRa Applications ▪

Currently applications are out of LoRa network, up to application providers to manage them on their own

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Each application can only see the data from LoRa devices in its own domain

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Problems for LoRa application developers • • •

Must deal with the complexity of orchestrating their LoRa applications No easy way to share and utilize IoT data across LoRa applications Northbound APIs to Applications are not standardized

LoRaWAN device1

appKey1

App1 Network server

gateway

App2

LoRaWAN device2 appKey2 1

Requirements of LoRa Applications ▪

Easy, fast, and on-demand orchestration of LoRa applications • •

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Standardized data model and APIs for data sharing across LoRa applications • • •

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Developers do not have to manage their resources and execution environments Low operation effort and fast time-to-market

Applications are always changing/evolving over time to fit various requirements Easy and standardized way to share IoT data with various applications Saving re-engineering effort and maximizing the value of data and device assets

Better management and control of how IoT data from LoRa devices can be shared and utilized • •

Data owners/device owners should still have the full control of how their data can be shared across applications/business domains Secure data sharing and privacy-preserving

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FIWARE FogFlow GE: Cloud-Edge Orchestrator  FogFlow is a cloud-edge orchestrator to orchestrate dynamic NGSI-based data processing flows on-demand between producers and consumers for providing timely results to make fast actions with low management cost and bandwidth consumption, based on context (system context and data context) System context cloud fast actions timely results

FogFlow Context availability (metadata)

edge

dynamic processing flows edge

edge

raw data

Producers (sensors)

Consumers (actuators)

FogFlow: Enabling Orchestration & Data Sharing of LoRa Apps Given the assumption that we use NGSI as the northbound API of LoRa applications for the purpose of easy and standardized data sharing, FogFlow can help them in three types of scenarios: ▪

Scenario 1: orchestrating LoRa applications purely in the cloud

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Scenario 2: orchestrating LoRa applications over cloud and edges

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Scenario 3: orchestrating LoRa applications across multiple LoRa networks 4

Orchestrating LoRa Applications in the Cloud External APPs

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Mainly for LoRa network operators • •

NGSI

Easy, fast, efficient orchestration of LoRa APPs for data processing Easy data sharing across APPs FogFlow

LoRaWAN device1

Cloud(s)

APP1 APP2

Network server

gateway

adapter APP3

LoRaWAN device2

FogFlow APPs: data processing flows for data integration, transformation, aggregation, and analytics 5

Orchestrating LoRa Applications over Cloud and Edges ▪

Edge analytics at LoRa/LoRaWAN gateways • •

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• •

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Constraint connectivity and communication cost between cloud and edges Edge nodes with sustainable power supplier Developing countries or the areas with limited network infrastructure

Value proposition ▪

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edge

Targeted scenarios •

Cost saving

Use case domains •

cloud

Moving LoRaWAN Network Server down to the edge Launching data processing directly at the edge

Smart agriculture or forest monitoring 6

NS GW

FogFlo w (cloud)

Limited connectivity (4G)

Fog Flow (edge)

edge NS GW

Fog Flow (edge)

Use Case: Forest Monitoring

Detecting & monitoring the activities of bears

Forest fire detection

cloud Autonomous edges

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Orchestrating LoRa Applications across LoRa Networks ▪

Utilizing data from different LoRa networks • •

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LoRa devices are deployed and managed by different providers; Federated data space across multiple LoRa networks

Value proposition

FogFlo w (cloud)

P3 FogFlow (edge)

P2

Seamless data usage for IoT services

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Trust data sharing across applications

Domain B

FogFlow NS

(edge)

▪

P1 Domain A

NS

Use cases: •

Data processing flows orchestrated by FogFlow

GW

smart parking across cities

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GW

Use Case: Smart Parking

Real-time parking recommendation

Real-time Traffic information from transportation provider Parking sensors

Context information from different LoRa networks operated by different owners 9

A Concrete Use Case: Waterproof Amsterdam

Sylvain Prost (The Things Industrie)

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TTN LoRaWAN Build your own LoRa network

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TTN LoRa Infrastructure

LoRa Gateway in Amsterdam

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Use Case: Waterproof Amsterdam

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Use Case Implementation: Waterproof Amsterdam

data processing flows running in the cloud

analytics

adapter

actuator

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Demo at Our Booth: FogFlow + LoRa ▪

Enabling smart solutions across federated city domains using cloud-edge computing and LoRa networks

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Showcasing smart city solutions based on a federated city-dataspace • •

Waterproof Amsterdam: Integration of FIWARE FogFlow with LoRaWAN Smart Awning and Smart Parking: Automated and optimized data flow orchestration with low development and management costs

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Thank you!