IoT platform as a regular server software. Types of platforms and its' communicaion with devices. Data normalization, storage, processing and visualization. IoT Platform Enterprise Integration. AggreGate Platform.

1. Internet
of Things
Anatomy

2. Marketing experts introduced
the Internet of Things
• There was no revolution, just evolution
• ‘Things’ have been communicating for quite a while (e.g. PLCs
on a wire drawing line or network switches)
• Monitoring and management systems have been existing for
long, but again marketing experts sent them to the ‘cloud’
• Cellular and satellite modems weren’t invented yesterday
• In fact IoT is just a general name joining various markets, both
B2B and B2C
• Terminology evolution:
Intelligent Device Management => M2M => IoT
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3. Internet of Things comprises
Devices (“things”)
Data centers
M2M concept assumes that devices interact with one
another. They can do it:
1) Directly via network
2) Via network and central software in a data center (in the
‘cloud’)
3) Sometimes both
Networks
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4. Device Network Structure
IP
TCP, UDP
SNMP, Telnet, BACnet, Modbus, SOAP, HTTP, MQTT…
RS-232, RS-485, Ethernet, Wi-Fi, USB, CAN, Bluetooth
Z-Wave, GPRS/3G/LTE…
PPP, ATM, SLIP…
NetBIOS, PPTP, RPC…
SSL, TLS...
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OSI Network Model

5. Device Types
The difference is in management
software tasks.
Example: GPS trackers for a dog and a bus are similar in
terms of hardware, but they have absolutely different
could services and dashboards.
Consumer Industrial
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6. Device Logical Structure
Variables (settings, properties):
ability to read and write
Such device structure is described in full or partially by
any known communication protocol.
Functions (methods, operations): ability to call and
transmit input data while receiving output data
Events (notifications): ability to subscribe and retrieve
instances asynchronously
Metadata (descriptions of available variables, functions
and events)
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7. Internet of Things Platform
• IoT platform is just a regular server software
• It plays a role of runtime environment (application server) for IoT
applications designed for the end user
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• Only a few applications are written
from scratch
(will cover the reasons later)
• IoT platforms are often deployed in
rented commercial data centers, or
in data centers belonging to large IoT
device operators

8. Primary Objectives of IoT Platforms
• Data collection from various devices and data sources
• Storage of externally collected as well as internally generated data
• Stand-alone data processing and automatic decision taking
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• Data visualization (developing
an operator interface)
• Enterprise data integration (only
for Industrial IoT)
• Intelligent data exchange
between devices

9. Types of IoT Platforms
• Infrastructure platforms provide data storage
and collection as well as API/SDK for
implementing processing, visualization and
integration methods (IoT application
development) via programming
• Full cycle platforms solve all tasks using visual
constructors, with the only necessity for
programming when writing communication
modules and complex mathematics/logic
- 9 -

10. Communication with Devices
• Any IT (SNMP, Telnet, WMI...), automation (Modbus,
BACnet, OPC…), IoT (MQTT, XMPP, AMQP…) and
universal (HTTP/REST, SOAP, FTP…) protocols are used
• Very few basic operations: reading and writing settings,
executing operations, receiving events (including
notifications on change in values)
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11. Data Normalization
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Normalization is conversion to a unified standard
form.
It’s usually performed in two steps:
• Abstraction from protocol (conversion to universal data types)
• Abstraction from device type/make/version (application of device models)

12. Data Storage
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What we store:
• Server-side configuration and tools
• Last device configuration snapshots
(in case of unavailability)
• Setting change history
(for devices and server-side tools)
• Event history (the same as above)
Where we store:
• Relational database (slow and inefficient)
• NoSQL database (оптимально)
• Specialized databases (e.g. RRD for time series
aggregation – has its own pros and cons)
RDBMS
RRD (Statistics)
NoSQL (Big Data)

13. Data Processing
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• Completely standalone
• Delayed group configuration and operation execution
• Operator notifications upon important events and states
(emails, SMS)
• Dynamic models with own life cycle
• Machine-readable knowledge base for taking decisions
• Multiple tools (root cause analysis, scheduler, domain-
specific languages – examples: AggreGate and IEC
languages)

14. Data Visualization
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• 1st and 2nd line operator interface is
built from scratch for each IoT
application
• Interface base is a set of dashboards
with navigation and drilldown
• Dashboards include tables, forms,
maps, facility plans, charts, diagrams
and many other components
• Everything is customizable till the very
last pixel
• Dynamic thanks to binding UI
components to properties and events
of a server data model

15. IoT Platform Enterprise
Integration
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• Uses the same protocols as for data collection
• The protocols work the other way round
• IoT doesn’t have
typical integration
scenarios
• Configuration should
be flexible but
without programming

16. Why not to write everything
yourself?
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• A prototype will be ready quickly
• You will spend years implementing a scalable system
supporting failover clustering, distributed collection and
storage architecture, etc.
• A bicycle will be invented in about 5 years
• There’ll be fixed expenses to support the real product state
• It looks even more unnatural for system integrators,
engineering companies and MSPs

17. Tibbo Systems and AggreGate
Platform
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• Tibbo Systems: Russian software developer working
worldwide
• AggreGate Platform: software “brick set” for building
IoT device monitoring and management systems
• 14-years’ investments into “brick” development
• Hundreds of large installations in various countries
• 10+ vertical market solutions, including IT
infrastructure management and SCADA systems

18. Cases and References
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• Monitoring and managing telecom tower power supply
(Flexenclosure, Sweden)
• Monitoring mission-critical uninterruptible power supply units
(Unified Energy Corporation, Russia)
• Narrow-band radio station monitoring system (DCI Tech, Canada)
• Comprehensive monitoring of a multi-server telecom operator
network (An-net, Russia)
• Monitoring of engineer constructions (Insight, Russia)
• Centralized fountain management (Sharel, Israel)
• Roadheading equipment monitoring (Ilma, Russia)

19. Cases and References
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• Building automation of the Electoral Commission of Namibia
• Data acquisition from industrial alcohol breath testing devices
(Intoximeters, the US)
• Forklift fleet management and monitoring (Keytroller, the US)
• Monitoring McAuto queue length and POS equipment
(McDonald’s, the US)
• Centralized monitoring, control and provisioning of Android-based
vending machines (Minibar Systems, the US)
• Cloud-based Time and Attendance system (RCPOnline, Poland)
• Monitoring of a distributed IP-based emergency notification
speakers network (Emergencies Ministry of Russia)