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Smart Cities, Smart Power
Stan Schneider
RTI CEO
IIC Vice Chair
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RTI lives at the intersection of functional artificial intelligence and pervasive networkingSM
Edge Autonomy IIoT
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DeviceMonitoring Edge
Autonomy
Databus
Databus
AnalyticOptimization
44
16%
19%
20%
20%
17%
8%
Indra / Minsait nº1 Spanish IT Company…
+140Countries
+€3,011 M€ (3.43BnUSD)
Sales
40,000Professionals
Traffic & Transport
Telco & Media
Security & Defense
Financial Services
Public Administration
and Health
Sales 2017
Energy & Industry
…and one of the largest in Europe and LatAm, with high growth in Asia Pacific and Africa
The IIoT architecture must span industries
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6
Monash University• Australia’s largest University
• 75,000+ students
Clayton Campus, 1.1 km2…so big that it has its own post code Net zero carbon emissions by 2030
135 M AUD of investment 2017-2030
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Monash MicrogridSmart Energy City Project
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Smart Energy City Project
2018 - 2020
Centralised control
Distributed control
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MONASHAdvanced Microgrid solution
Distribution
Grid Domain
National market domain
Microgrid domain RT Power Quality Management :
Quality + reliability
…
Local energy services market
PlanningForecast Scheduling ExecutionEnrolmentAudit &
settlement
Industrial IoT architecture
Edge Dynamic Energy Management.
Microgrid Power Quality Management.
Key features
Microgrid energy and power market.
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Data flow equirements
Interoperability across several proprietary systems
Data are often both critical and real-time
Avoid bottle necks
Cloud/edge hybrid solution
“Edge” often meant limited processing capacity
Connected systems and components need only to receive and elaborate some of the data
Large volume of real-time data as well as corporate business data (System-of-Systems)
Cybersecurity is an obligation
Quality of Service (QoS) characteristics
Data spaces (domains, partitions and topics) and data structures
IIC Layered Databus Architecture Pattern
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The Grid needs to evolve to unlock new value of energy services
The systems supporting the operation and corporate processes need to go through a digital transformation in the effort to adapt to functionalities that go from centralized to distributed with an increasing amount of coordination.
The implementation of DISTRIBUTED models through intensive use of edge computing is at its very beginning in the power utility industry. There are plenty of great opportunities.
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Implementation
Big DataAnalytics
Edge Intelligent
Nodes
RT Unified Data Space
RT Monitoring & Control
Grid Cold PathAnalytics
Primary & Secondary Substations
Demand Management (Home, Building,
Industry)
Distributed generation Storage, EV infrastructure
RT Power AnalyticsEngine
Grid AnalysisOptimizationPower quality
Close loop automation
GridPerformance
AssetPerformance
DMS
AMI
SCADA
Grid Sensors,analyzers,
Fault detectors, etc.
Implements “3 tier” with “layered databus”
Databus in Renewables
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RTI runs the largest hydropower plants in North America, soon expanding to 75 dams.
RTI controls Siemens Wind Power’s most advanced wind turbines, thousands deployed worldwide.
RTI is a principal in the leading new grid standard for solar and battery integration.
RTI today controls >24GW of renewable power, displacing 124m metric tons of C02/year
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Databus Applications Span the IIoT
What is the Industrial IoT Really About?
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Combining technologies from
all industries
To make all industries better
The Architectural Challenge
Create a powerful abstraction to effect integration with minimal extraneous coupling
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Put Data in the Center
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Application
Application
Message centricClient/ServerRemote ObjectsPublish-subscribeSOA
DDS is the standard that defines a databus
Data-centric technology connects applications to
the data, not to each other
Application
Application
Data
Application
“Data Everywhere” Abstraction Enables Sharing
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• Doesn’t actually send all the data…
• Every application gets everything it needs, when it needs it– Applications declare
needs and capabilities– Databus delivers data
• Applications interface only to data– Every app speaks its own
language– Databus maps language,
CPU, OS, transport
• Fast, reliable, scalable
Shared Global Data Space
DDS DataBus
DataTopicTypeQoS
DataTopicTypeQoS
DataTopicTypeQoS
Naturally, massively parallel Like distributed shared memory
Data-Centric Decoupling Separates Applications
• Flow: discovery, flow rates, reliability uncoupled
• Space: services live anywhere
• Time: participants come & go any time
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Because the interface is only to the data…applications are decoupled.
And, Since Systems are All About The Data…
• Data-centric sharing lets subystems cooperate
• Decoupling lets them work independently
Resulting in…
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Shared Global Data Space
DDS DataBus
What is Data Centricity, Really?
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All the Data is Right There in Every Device and Algorithm
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The essence of data centricity is “instant” access to absolutely anything and everything by every device and every algorithm,
at every level, at any time.
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
C++iOS
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
ARM
CRTOSARM
C++QNXx64
C#Winx86
JavaAndroid
ARM
CLinuxPPC
ADARTOSPPC
C++VxWorks
x86
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
struct StationValues {
unsigned long ID; //@key
unsigned long count;
float Temperature;
};
JavaOS Xx64
C++Linuxx64
And All in Each Application’s Native Format
TCP
TCPUDP
UDP
UDP UDP
UDP UDP??
??
Shared Global Data Space
DDS Databus
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Distributed Architectures Support Higher Autonomy
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Data Centricity enables new architectures that are fast, distributed, and reliable.
Autonomy Requires Subsystems
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Cloud Services
Sensing
Planning
Radar, LIDAR Vehicle Platform Navigation
Error Management
Visualization
Situation AnalysisSituation Awareness
Vision FusionCameras, LIDAR,
Radar …
Data Fusion
LoggingVehicle Control
Localization
Databus
Traffic Maps
Databus
IIC Layered Databus Architectural Pattern
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In-Car Platform
V2V & V2X
Public Internet
Fleet Management
• End-User Applications• Connect with RESTful/Web Sockets
• Traffic Management• Traffic Light Control• Congestion Management
• Road Management• Environmental & Road Conditions
• Probe Data Collection (Sensors)• Environmental Weather• Dynamic Vehicle Location
• Collision Avoidance• Navigation• Safety Certified
Un
ifie
d D
ata
Mo
del
Think HMI
Sense Act
Machine Databus
Unit Databus
Site Databus
Cloud Databus
Matches architecture: no code!
Important, “bolted on”, not enough
Systems Need Integrated Data Defense
• System edge• Host
– Machine/OS/Applications/Files
• Network transport– Layer 2-3 “pipe” security– Layer 4-5 “session” security
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• DDS: Dataflow Security– Control application interaction– Secures the data itself; no API!– Independent of transport, OS, chip
Defense in Depth
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Root of Trust Defense
Edge Defense
Network Defense
Host Defense
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Securing the system without considering
the application is not a good de-fence
Selection Guide
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Simple Diagnostic Questions
http://
Base information source: IIC Industrial Internet Connectivity Framework (IICF)
You Need a Databus if You Face These Problems
• Are there severe consequences of failure for a few mins/secs?
• Have you said “millisecond” in the last 2 weeks?
• Do you have more than 10 software engineers?
• Does your data have many destinations?
• Are you building a next-generation IIoT design?
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3+ Yes?
©2017 Real-Time Innovations, Inc.
Early Databus IIoT Applications
• Transportation– Carbots– Rail & mass transit– Traffic control (ground & air)– Drones and personal aviation
• Energy– Renewable generation (hydro, wind, solar)– Renewable integration (Substation automation, DER
pilots, microgrids)– Automated drilling & production
• Medical– Intelligent connected medical devices– Surgical robotics– Imaging & treatment systems
• Defense– Avionics & UAS– Naval systems– Radar/antimissile/air defense– Ground vehicles– Asset tracking/C2
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RTI & The Industrial IoT
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• RTI is the largest IIoT connectivity vendor
• 1300+ designs, many real-world programs across industries
• Full DDS, tools, services, support, secure & certified versions
• ~200 ppl, $40+m
The Industrial IoT Disruption
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The real value is a common architecture that connects sensor to cloud, interoperates between vendors, and spans industries
You don’t compete against competitors. You compete against market transitions.
– John Chambers
Where Can I Learn More?
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ConnextCon Speakers!
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community.rti.com
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www.rti.com
Free for pre-commercial use
Thank You!
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Connect!!
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• [email protected]@RTIStanLinkedIn: Stan Schneiderhttps://www.linkedin.com/in/stan-schneider-102466/
• Bio– CEO Real-Time Innovations, Inc.– IIC Steering Committee Vice Chair
– Advisory Board, IoT SWC– Top-25 Global IIoT Influencer– PhD, EE/CS, Stanford