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FIWARE Global Summit - FogFlow Enabled Sharing across LoRa Applications


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Presentation by Bin Cheng (NEC Labs Europe) and Sylvain Prost (The Things Industrie)

FIWARE Global Summit
8-9 May, 2018
Porto, Portugal

Published in: Technology
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FIWARE Global Summit - FogFlow Enabled Sharing across LoRa Applications

  1. 1. FogFlow enabled Data Sharing across LoRa Applications Bin Cheng (NEC Labs Europe) Sylvain Prost (The Things Industrie)
  2. 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. 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. 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. 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. 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. 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. 8. Use Case: Forest Monitoring 7 cloud Forest fire detectionDetecting & monitoring the activities of bears Autonomous edges
  9. 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. 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. 11. A Concrete Use Case: Waterproof Amsterdam Sylvain Prost (The Things Industrie) 10
  12. 12. TTN LoRaWAN Build your own LoRa network 11
  13. 13. TTN LoRa Infrastructure 12 LoRa Gateway in Amsterdam
  14. 14. Use Case: Waterproof Amsterdam 13
  15. 15. Use Case Implementation: Waterproof Amsterdam 14 adapter analytics actuator data processing flows running in the cloud
  16. 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. 17. Thank you!