Interoperability for IoT is a challenging problem because it requires us to tackle (i) cross-system interoperability issues at the IoT platform sides as well as relevant network functions and clouds in the edge systems and data centers and (ii) cross-layer interoperability, e.g., w.r.t. data formats, communication protocols, data delivery mechanisms, and perfor- mance. However, existing solutions are quite static w.r.t software deployment and provisioning for interoperability. Many middle- ware, services and platforms have been built and deployed as interoperability bridges but they are not dynamically provisioned and reconfigured for interoperability at runtime. Furthermore, they are often not considered together with other services as a whole in application-specific contexts. In this paper, we focus on dynamic aspects by introducing the concept of Resource Slice Interoperability Hub (rsiHub). Our approach leverages existing software artifacts and services for interoperability to create and provision dynamic resource slices, including IoT, network functions and clouds, for addressing application-specific interoperability requirements. We will present our key concepts, architectures and examples toward the realization of rsiHub.

1. Towards a Resource Slice
Interoperability Hub for IoT
Hong-Linh Truong
Faculty of Informatics, TU Wien, Austria
hong-linh.truong@tuwien.ac.at
http://rdsea.github.io
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2. Acknowledgements:
Supported by
H2020 Inter-IoT (http://www.inter-iot-project.eu/)
Lingfan Gao and Michael Hammerer for
implementating prototypes and creating demos
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3. Outline
 Motivating examples and approach
 Key concepts
 Architecture and components
 Prototype and demo
 Conclusions and future work
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4. Related work and our approach
 IoT Interoperability problem is known, but
 addressed through the deployment of services and
components after a long cycle of software
development and operation
 hard to dynamically reconfigure and support
opportunistic interoperability solutions
 lack of customization: interoperability are also user-
specific problems, not just standardization issues
 often isolated from runtime services management
 Our contributions: dynamic solutions based on
ensembles/resource slices
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5. Motivating scenarios
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 Many things can establish very fast: querying camera,
downloading video, transferring data and launching analytic
components
 Large-scale on-demand video analytics need responsive and
reactive interoperability w.r.t data conversion, protocol
translations and data contracts

6. Motivating scenarios
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 Similar issues: data contract, conversion, quality of data, and transfer protocols
 But interoperability cannot work without network engineering and data protection

7. Key requirements
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 Cross systems and cross layers:
 Interoperability is not just needed IoT resources
 We need IoT, network functions and cloud resources
 A combination of issues w.r.t. data, protocols, contracts,
service quality, etc.
 Developer goals:
 Reduce time to establishing interoperability solutions
 Configure suitable components to deal with interoperability
 Deploy and provision interoperable bridges
 Address application-specific concerns
 Aim to develop full automatic solutions

8. Ensemble and its resource slice
 Resources: data streams, analytics, broker, storage, etc.
 Any existing software and service supporting
interoperability is also a resource
 A composition of “non-Interoperable components“ can
create a virtual resource for interoperability
„interoperability bridge“ as a resource
Resource slice
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Resource slice concept and related papers: http://sincconcept.github.io/

9. Key concepts
 Client c specifies a resource slice: RS(c)
 We make the resource slice interoperable,
creating RSi(c) from RS(c)
 We focus on resources can be represented by
data points and control points with suitable
metadata: resource is r(DP,CP,MT)
 DP (data points), CP (control points), MT (metadata)
 A service provider must provide enough metadata of its
resources
 We must be able to deploy a software artifact supporting
interoperability on-demand
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10. Examples
 DP(r)= {dpa,dpc} return all videos and the current
video, and CP(r)={cpp } put video to a storage
 A service S can provide a set of R={ri} allow to use
DP(r) and CP(r)
 Examples of slices
 RS(c) ={ri, GoogleStorage, FaaS}
 RS(c) ={ri, Kafka, Trigger, Container}
 Our interoperable slice structures: many-to-one, one-to-
one, one-to-many
 Augment RS(c) with IBE(c)={ibei} RSi(c)through
analytics, recommendation and composition
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11. Integration requirements for
dynamic interoperability
 Resource providers:
 Instances of resource providers provisioning
resources
 Resources and providers can be controlled at
runtime
 Repository for artifacts for interoperability
 Artifacts can be instantiated into the right
environments
 E.g., a middleware service for performing protocol
translation, a data pipeline for covering data, or a
function for filtering IoT data
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12. Resource Slice Interoperability Hub
architecture
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13. Enriching Client’s Resource slice
 Dynamic interoperability solutions cover
 Interoperability analytics
 Recommendation and composition
 Resource provisioning and configuration
 Metadata focused on runtime aspects
 Data quality, data delivery, data compliance regulations, data
transfer protocols
 Interactions for interoperability
 Data transformation
 Protocol translation for data delivery
 Data contract assurance
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14. Resource abstractions and
metadata for interoperability
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 Extension of our HINC model [FICloud 2016]
 Added new metadata for interoperability context: DataPoint,
ControlPoint, ExecutionPolicy, DataQuality, and Connectivity

15. Metadata
 Typical resource metadata and “interoperabity
metadata” but in an extensible model
 We have more interesting novel aspects of
metadata reflecting dynamics for interoperability
solutions, e.g., quality, contract, delivery
frequency (related to V* of data) rather than
traditional static “protocols” and “formats”
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16. Examples of metadata
 Resource type
• SENSOR, ACTUATOR; MESSAGEQUEUE, FIREWALL, FILE_STORAGE, VPN,
CONTAINER, VIRTUALMACHINE
• FAAS, INTEROPERABILITY_BRIDGE, DATA_TRANSFORMATION,
PROTOCOL_TRANSLATION
 Protocols:
 REST, MQTT, AMQP, CoAPP
 IP, LoRaWAN, NB-IOT
 Data access patterns: PubSub (fan-out), ReqResp, Queue
 Data contract
 Data quality: ACCURACY, TIMELINENESS, etc.
 Data delivery: FREQUENCY (e.g. sampling rates)
 Execution policy and data regulation: e.g., within EU
 Data format: CSV, JSON, AVRO, RDF
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17. Resource metadata at runtime
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Sensor encapsulated in datapoints
Software artifact for interoperability
*Some sensitive information removed

18. Key steps in interaction in solving
interoperability
Key steps are reflected in interoperability analytics,
recommendation and provisioning
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19. Provisioning and Configuration
 Microservices with containers and virtual
machines suitable for IoT, network functions
and clouds
 Extensibility
 not all services are developed by us
 Leverage static interoperability solutions at runtime
 Different layers and models, e.g.,
 Long running services based on specific protocols, like
messaging, storage, queues, and REST web services
 Function-as-a-service, based on event triggers
 Batch workflow/pipeline styles
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20. Examples with Base Transceive
Station Slice
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Other examples of runtime operations:
 Network functions like firewall can be added into the pipeline
 Traffics at NFV can be reengineered.

21. Current prototype
 rsiHub prototype
 https://github.com/SINCConcept/HINC
 Examples of resource providers, including for
interoperability solutions
 https://github.com/rdsea/IoTCloudSamples
 Video demo
 A slice with BTS with data transformation
 https://storage.googleapis.com/demovideorsihub/de
mo_1080.mp4
 By Lingfan Gao and Michael Hammerer
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22. Conclusions and future work
 Dynamic interoperability solutions fill the gap
 Provide on-demand solutions (short time to develop)
 Opportunities to utilizing existing solutions with
others
 But we need to consider system-as-a-whole:
“resource slice” view
 Future work
 Interoperability analytics and recommendation
 Further metadata development
 DevOps IoT Interoperability as a software
engineering approach for IoT interoperability
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23. Thanks for your
attention!
Hong-Linh Truong
Faculty of Informatics
TU Wien, Austria
rdsea.github.io
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