Dominik Obermaier | CTO & Co-Founder,
HiveMQ
Anja Helmbrecht-Schaar | Senior Consultant,
HiveMQ
IIoT Monitoring with
MQTT Sparkplug,
HiveMQ and InfluxDB

© 2021 InfluxData. All rights reserved. 2
| IIoT Monitoring - with MQTT Sparkplug, HiveMQ and InfluxDB
Dominik Obermaier
HiveMQ CTO & Co-founder
@dobermai linkedin.com/in/dobermai/
Anja Helmbrecht-Schaar
Senior Consultant
linkedin.com/in/anjahelmbrechtschaar/

© 2021 InfluxData. All rights reserved. 3
| Introduction to HiveMQ
• Founded in 2012, based outside of Munich
• HiveMQ helps move data to and from connected devices in an
efficient, fast and reliable manner
• 130+ customers with production IoT applications

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| HiveMQ Platform
● High availability
● 100% MQTT compliant
● Scalability
● Observability
● Enterprise Security

And the Gap between IT and OT
Status Quo in IIoT

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| Lots of Data Silos

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| Coupled Infrastructure

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| Siloed OT Systems - No Interoperability

| Challenges
● Difficult to change workflows and processes
● Difficult to setup a new system/facility
● Difficult to analyze data across the entire system

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| Decoupled Architecture

| Decoupled clients and broker
| Publish/Subscribe protocol
| Extensible
| Reliable

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| Pub/Sub Pattern

| There are still issues
● Devices and endpoints have different topics, payloads and
data structures
● Applications assuming specific formats and structure
● Data agnostic, payload must be interpreted but no
context

A simple, open specification, that will enable plug and play
interoperability between IIoT devices and IIoT applications.
Introducing Sparkplug
Sparkplug defines:
● The Topic namespace
● A Data Model and Structure
● An extensible process variable payload
● MQTT state management

KEY CONCEPTS
Sparkplug
● Continuous Session Awareness
● Report by Exception
● Interoperability by consistent data format
● Auto Discovery

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| Sparkplug enabled Architecture

● Ideal for real time data
● Direct Integration
● Includes Monitoring Dashboard
● Could Replace Historian and Analytics
Adding InfluxDB

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| HiveMQ with Sparkplug & InfluxDB

| DEMO

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| Sparkplug Scenario simulated with HiveMQ & InfluxDB
Sparkplug
Extension
hivemq

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| Sparkplug Scenario - what was visualized
All Participants send data in a predefined protobuf schema
Devices forward Data to an
EoN
The topic structure is predefined namespace/group_id/message_type/edge_node_id/[device_id
]
The message types are predefined

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| Sparkplug Scenario - Participants
spBv1.0/location1/ STATE/Scada_1
$messageType
[NBIRTH,DBIRTH,NDATA,DDATA,NDEATH,DDEATH]
spBv1.0/location1/ $messageType /$eonId/$devId
spBv1.0/location1/ $messageType /$eonId
spBv1.0/location1/#
spBv1.0/location1/DCMD/$eonId/#
spBv1.0/location1/NCMD/$eonId/#
Subscribe topics
Publish topics
Connect EoNs and Subscribe
Publish BIRTH per device & EoN
Publish DATA per device & EoN
Publish DEATH per device & EoN
Disconnect EoNs that triggers DEATH message
Connect Scada Host and Subscribe
Publish STATE
Publish Command data
MQTT Messages
MQTT Clients
Scada_1
$eonId
$deviceId
spBv1.0/location1/NCMD/ $eonId
spBv1.0/location1/DCMD/ $eonId/$devId

© 2021 InfluxData. All rights reserved. 23
| Metric & Device data visualization - Sparkplug InfluxDB Extension

| Further Information
● Download HiveMQ: https:/
/www.hivemq.com/downloads/
● Our marketplace: https:/
/www.hivemq.com/extensions/
● MQTT Essentials: https:/
/www.hivemq.com/tags/mqtt-essentials/
● Sparkplug Essentials: https:/
/www.hivemq.com/mqtt-sparkplug-essentials/
● Github: https:/
/github.com/hivemq
● Contact HiveMQ: contact@hivemq.com

Questions