Presentation by Bin Cheng (NEC Labs Europe) and Sylvain Prost (The Things Industrie) FIWARE Global Summit 8-9 May, 2018 Porto, Portugal

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

2. 1
LoRa Applications
▪ Currently applications are out of LoRa network, up to application providers to
manage them on their own
▪ Each application can only see the data from LoRa devices in its own domain
▪ 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
gateway
Network
server
App1
App2LoRaWAN
device2
LoRaWAN
device1
appKey2
appKey1

3. Requirements of LoRa Applications
▪ Easy, fast, and on-demand orchestration of LoRa applications
• Developers do not have to manage their resources and execution environments
• Low operation effort and fast time-to-market
▪ Standardized data model and APIs for data sharing across LoRa applications
• 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
2

4. 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)
Producers
(sensors)
Consumers
(actuators)
cloud
edge edge edge
raw data
timely results
fast actions
FogFlow dynamic
processing
flows
Context availability
(metadata)
System context

5. FogFlow: Enabling Orchestration & Data Sharing of LoRa Apps
▪ Scenario 1: orchestrating LoRa applications purely in the cloud
▪ Scenario 2: orchestrating LoRa applications over cloud and edges
▪ Scenario 3: orchestrating LoRa applications across multiple LoRa networks
4
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:

6. Orchestrating LoRa Applications in the Cloud
5
gateway
Network
server
LoRaWAN
device2
LoRaWAN
device1 Cloud(s)
FogFlow
APP1
adapter
APP2
APP3
External APPs
FogFlow APPs: data processing flows for data
integration, transformation, aggregation, and analytics
NGSI
▪ Mainly for LoRa network operators
• Easy, fast, efficient orchestration of LoRa
APPs for data processing
• Easy data sharing across APPs

7. Orchestrating LoRa Applications over Cloud and Edges
▪ Edge analytics at LoRa/LoRaWAN gateways
• Moving LoRaWAN Network Server down to the
edge
• Launching data processing directly at the edge
▪ Targeted scenarios
• 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
▪ Cost saving
▪ Use case domains
• Smart agriculture or forest monitoring
6
cloud
edge edge
Limited
connectivity (4G)
GW
NS Fog
Flow
(edge)
FogFlo
w
(cloud)
GW
NS Fog
Flow
(edge)

8. Use Case: Forest Monitoring
7
cloud
Forest fire
detectionDetecting &
monitoring the
activities of bears
Autonomous
edges

9. Orchestrating LoRa Applications across LoRa Networks
8
▪ Utilizing data from different LoRa networks
• LoRa devices are deployed and managed by
different providers;
• Federated data space across multiple LoRa
networks
▪ Value proposition
▪ Seamless data usage for IoT services
▪ Trust data sharing across applications
▪ Use cases:
• smart parking across cities
Domain A
Domain B
GW
NS
GW
NS
P1
P2
Data processing
flows orchestrated by
FogFlow
P3
FogFlo
w
(cloud)
FogFlow
(edge)
FogFlow
(edge)

10. Use Case: Smart Parking
9
Parking
sensors
Real-time Traffic
information from
transportation
provider
Real-time parking
recommendation
Context information from different LoRa networks
operated by different owners

11. A Concrete Use Case: Waterproof Amsterdam
Sylvain Prost (The Things Industrie)
10

12. TTN LoRaWAN
Build your own LoRa network
11

13. TTN LoRa Infrastructure
12
LoRa Gateway in Amsterdam

14. Use Case: Waterproof Amsterdam
13

15. Use Case Implementation: Waterproof Amsterdam
14
adapter analytics
actuator
data processing flows
running in the cloud

16. Demo at Our Booth: FogFlow + LoRa
▪ Enabling smart solutions across federated city
domains using cloud-edge computing and LoRa
networks
▪ 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
15

17. Thank you!