LoRaWAN and edge analytics

1. Enabling Edge Analytics of IoT Data:
The Case of LoRaWAN
Hong-Linh Truong
Faculty of Informatics, TU Wien, Austria
hong-linh.truong@tuwien.ac.at
http://rdsea.github.io
@linhsolar
Global IoT Summit 2018, 4 April 2018, Bilbao 1

2. Acknowledgements:
Supported by
H2020 Inter-IoT (http://www.inter-iot-project.eu/)
Tran-Vu Pham, Danh Pham, Phuong Nam Ho and
Xuan-Hiep Huynh for discussions on LoRaWAN and
aqua/argiculture monitoring in Vietnam
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3. Outline
 State of the art, motivating examples and
approach
 IoTRACE key concepts, architecture and main
components
 Prototype and illustrating examples
 Conclusions and future work
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4. New network technologies for
large-scale monitoring with IoT
 Large-scale deployment of low-energy sensing
devices with long distance communications
 LoRaWAN, SigFox and NB-IoT
 Currently two main deployment models
 Big providers of IoT infrastructures and platform
connectivity
 like Sigfox, telcos or government infrastructures
 Community with many stakeholders
 like The Things Network (TTN)
 But these deployment models are not flexible
enough w.r.t IoT data services and edge analytics
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5. LoRaWAN
 We focus on LoRaWAN because of its availability
and investment cost in developing country context
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 Suitable for us to work on large-scale geographic
areas and with no requirements of high bandwidth
 Agriculture/Aquaculture productivity and safety
monitoring
 Saltwater intrusion impact monitoring
 Wastewater monitoring
 Smart city monitoring

6. Data/analytics requirements and
issues
 Farmers want to buy data and might
provide/share devices but only related
to their own local space
 Sharing data for local view among
farmers/local agencies and for global
view needed by government/agency
 Cloud is expensive when there is no
need for farmers/local view
 LoRAWAN network-centric designs:
 Centralized analytics is not
suitable
 IoT data-as-a-service and IoT
infrastructure-as-a-service are not
well support with multiple
stakeholders
Global IoT Summit 2018, 4 April 2018, Bilbao 6
Picture from Tran-Vu Pham for
shrimp/fish/lobster farms monitoring

7. Why do we augment LoRaWAN?
 Support IoT service models:
 data-as-a-service model and infrastructure-as-a-
service at the edge
 Support edge analytics with IoT data
 Work along with current LoRaWAN features for
clouds: edge + cloud
 Focus on data analytics workflows as the main
LoRaWAN applications
 We concentrate on software architecture design
and configuration for edge analytics
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8. IoTRACE Framework
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Key design: making data available in the edge requires
many changes in data flows and stakeholders management
Enable edge
computing

9. Stakeholders management for IoT
data services
 Model stakeholders to support IoT data-as-a-
service + IoT infrastructure-as-a-service
 Devices + data contracts
 Enable extraction of data at the edge for edge
computing
Global IoT Summit 2018, 4 April 2018, Bilbao 9

10. Network Server enabling edge
computing
 Forward message to edge analytics servers
 Extract application data at the edge
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11. Extracting data in the edge
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Limitation mixed model:
only subscription of devices

12. Current prototype
 Testing purposes: emulated devices/sensors
 Using LoRaWAN python library
(https://github.com/jeroennijhof/LoRaWAN )to create
LoRa messages
 Send to gateway via queue or to Packet Forwarder
using LoRa-Gateway-Bridge
(https://www.loraserver.io/lora-gateway-
bridge/overview/)
 Emulating sensors read real dataset or emulate
real sensors
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13. Current prototype
 Gateway and Network Server using python and
MQTT
 Only for proof-of-concept of the features we augment
 Subscribers and data contracts managed by
MongoDB
 Edge analytics components and functions:
 Python-based libraries and queues
 Javascript is being developed
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14. Simple example of analytics
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Analyti
cs
IoTRACE python
libraries for edge
computing
deploy

15. Node-RED flows accessing edge
data queues
 Edge analytics is written in Node-RED
 Application user deploys adaptor to the edge
server working with Network Server
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16. Cross edge cloud analytics example
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Example Apex code
We still need to mange
two sides separately
The queue
identification for linking
the edge to cloud is
application-specific
Cloud analytics

17. Conclusions and future work
 We enable diverse types of IoT service models and
different configurations for LoRaWAN
 Augment software architectures for enabling IoT data-
as-a-service and infrastructure-as-a-service at the edge
 Provide mechanisms for enabling edge computing for
IoT applications
 Future work
 Refinement of architectures and data contract models
 Data interoperability for edge analytics for network of
Network Servers
 Serverless functions for analytics & performance tests
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18. Thanks for your
attention!
Hong-Linh Truong
Faculty of Informatics
TU Wien, Austria
rdsea.github.io
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