This presentation introduces the coordination model at the foundation of Vortex and explains its foundational concepts and features. Then it provides an overview of the various technological element that implement the model and how they are deployed in IoT applications such as connected vehicles, smart cities, smart grids and connected medical devices.

1. Data Sharing in Industrial IoT
Angelo Corsaro, PhD
Chief Technology Officer
angelo.corsaro@prismtech.com

2. Vortex is a standard technology for
efficient, ubiquitous, interoperable,
secure and platform independent data
sharing across network connected
devices
in151
Characters

3. Vortex's
Coordination Model

4. Applications can autonomously
and asynchronously read and
write data enjoying spatial and
temporal decoupling
DDS Global Data Space
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
Global Data Space

5. Built-in dynamic discovery
isolates applications from
network topology and
connectivity details
DDS Global Data Space
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
Dynamic Discovery

6. The relevant portion of the
data space is projected on
the application address
space. Each typed projection
is commonly called a Cache
No single point of failure or
bottleneck
Decentralised
Data Space
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
TopicD
QoS
TopicD
QoS
TopicA
QoS

7. Connectivity is dynamically
adapted to chose the most
effective way of sharing data
Adaptive Connectivity
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
TopicD
QoS
TopicD
QoS
TopicA
QoS
The communication between
the DataWriter and matching
DataReaders can be peer-to-
peer exploiting UDP/IP
(Unicast and Multicast)or
TCP/IP
The communication between
the DataWriter and matching
DataReaders can be
“brokered” but still
exploiting UDP/IP (Unicast
and Multicast)or TCP/IP

8. Topic

9. A domain-wide information’s
class A Topic defined by means
of a <name, type, qos>
Topic
DDS Global Data Space
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
Topic
Type
Name
QoS

10. Topic Types

11. Topic Types:
Language Independent
Definitions

12. Topic types can be expressed
using different syntaxes,
including IDL and ProtoBuf
Topic Type
struct CarDynamics {
string cid;
long x; long y;
float dx; long dy;
}
#pragma keylist CarDynamics cid
IDL

13. Topic types can be expressed
using different syntaxes,
including IDL and ProtoBuf
Topic Type message CarDynamics {
option (.omg.dds.type) =
{name: "CarDynamics"};
required string cid = 0
[(.omg.dds.member).key = true];
required long x = 1;
required long y = 2;
required float dx = 3;
required long dy = 4;
}
ProtoBuf

14. Topic Types:
Language Specific
Definitions

15. Topic types can be expressed
using different syntaxes,
including IDL and ProtoBuf
Topic Type
class CarDynamics:
constructor:
(@cid, @x, @y, @dx, @dy) ->
CoffeeScript

16. Topic types can be expressed
using different syntaxes,
including IDL and ProtoBuf
Topic Type
public struct CaDynamics {
public string cid { get; set; }
public int x { get; set; }
public int y { get; set; }
public int dx { get; set; }
public int dy { get; set; }
public CaDynamics (string cid,
int x, int y, int dx, int dy)
{
this.cid = cid;
this.x = x; this.y = y;
this.dx = dx; this.dy = dy;
}
}
C#

17. Topic types can be expressed
using different syntaxes,
including IDL and ProtoBuf
Topic Type
@KeyList ( topicType = "CarDynamics", keys = {"cid"})
public class CarDynamics {
public String cid;
public int x; public int dx;
public int y; public int dy;
public CarDynamics(String s, int a, int b,
int c,int d) {
this.cid = s;
this.x = a; this.dx = b;
this.y = c; this.dy = d;
}
@Override
public String toString() {
…
}
}
Java

18. Topics, Instances
and Samples

19. CopyrightPrismTech,2015
Each unique key value identifies
a stream of data
Vortex demultiplexes “streams”
and provides lifecycle
information
A DataWriter can write samples
belonging to multiple instances
Topic Instances
CarDynamics
Instances
struct CarDynamics {
@key
string cid;
long x; long y;
float dx; long dy;
}
Topic
Topic Type
Sample

20. QoS policies allow to express
temporal and availability
constraints for data
DDS Global Data Space
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
QoS - Enabled

21. A collection of policies
that control non-
functional properties
such as reliability,
persistence, temporal
constraints and priority
QoS
HISTORY
LIFESPAN
DURABILITY
DEADLINE
LATENCY BUDGET
TRANSPORT PRIO
TIME-BASED FILTER
RESOURCE LIMITS
USER DATA
TOPIC DATA
GROUP DATA
OWENERSHIP
OWN. STRENGTH
LIVELINESS
ENTITY FACTORY
DW LIFECYCLE
DR LIFECYCLE
PRESENTATION
RELIABILITY
PARTITION
DEST. ORDER
RxO QoS Local QoS

22. QoS Policies controlling
end-to-end properties
follow a Request vs.
Offered
QoS
Domain
Participant
DURABILITY
OWENERSHIP
DEADLINE
LATENCY BUDGET
LIVELINESS
RELIABILITY
DEST. ORDER
Publisher
DataWriter
PARTITION
DataReader
Subscriber
Domain
Participant
offered
QoS
Topic
writes reads
Domain Id
joins joins
produces-in consumes-from
RxO QoS Policies
requested
QoS

23. Interacting with
the Data Cache

24. Each Data Reader is
associated with a Cache
The Cache stores the last
n∊𝜨∞
samples for each
relevant instance
Data Cache
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS
TopicD
QoS
TopicD
QoS
TopicA
QoS

25. Each Data Reader is
associated with a Cache
The Cache stores the last
n∊𝜨∞
samples for each
relevant instance
Data Cache
DataReader Cache
DataReader
...
Samples
Instances
Cache

26. The action of reading
samples for a Reader Cache
is non-destructive.
Samples are not removed
from the cache
Reading Data
DataReader Cache
DataReader
...
DataReader Cache
DataReader
...read

27. The action of taking
samples for a Reader Cache
is destructive.
Samples are removed from
the cache
Taking Data
DataReader Cache
DataReader
...
DataReader Cache
DataReader
...take

28. Samples can be selected
using composable content
and status predicates
Sample Selectors
DataReader Cache
DataReader
...

29. Filters allow to control what
gets into a DataReader
cache
Filters are expressed as
SQL where clauses or as
Java/C/JavaScript
predicates
Content-Filtering
DataReader Cache
DataReader
...
Filter
Application
Network

30. Content Filters
can be used to
project on the
local cache only
the Topic data
satisfying a given
predicate
Content
Filters struct CarDynamics {
@key
string cid;
long x; long y;
float dx; long dy;
}
cid x y dx dy
GR 33N GO 167 240 45 0
LO 00V IN 65 26 65 0
AN 637 OS 32 853 0 50
AB 123 CD 325 235 80 0
“dx > 50 OR dy > 50”
Type
CarDynamics
cid x y dx dy
LO 00V IN 65 26 65 0
AB 123 CD 325 235 80 0
Reader Cache

31. Queries allow to control
what gets out of a
DataReader Cache
Queries are expressed as
SQL where clauses or as
Java/C/JavaScript
predicates
Content-Based
Selection
DataReader Cache
DataReader
...
Query
DataReader Cache
DataReader
...
Application
Network

32. Reader Cache
Queries can be
used to select out
of the local cache
the data matching
a given predicate
Queries struct CarDynamics {
@key
string cid;
long x; long y;
float dx; long dy;
}
cid x y dx dy
GR 33N GO 167 240 45 0
LO 00V IN 65 26 65 0
AN 637 OS 32 853 0 50
AB 123 CD 325 235 80 0
“dx > 50 OR dy > 50”
Type
CarDynamics
cid x y dx dy
GR 33N GO 167 240 45 0
LO 00V IN 65 26 65 0
AN 637 OS 32 853 0 50
AB 123 CD 325 235 80 0
cid x y dx dy
LO 00V IN 65 26 65 0
AB 123 CD 325 235 80 0
query

33. State based selection
allows to control what gets
out of a DataReader Cache
State base selectors
predicate on samples meta-
information
State-Based Selection
DataReader Cache
DataReader
...
State Selector
DataReader Cache
DataReader
...
Application
Network

34. Sample State (READ, NOT_READ)
allows to distinguish between new
samples and samples that have
already been read
State-Based Selection
DataReader Cache
DataReader
...
State Selector
DataReader Cache
DataReader
...
Application
Network

35. The View State (NEW, NOT_NEW)
allows to distinguish a new instance
from an existing one
State-Based Selection
DataReader Cache
DataReader
...
State Selector
DataReader Cache
DataReader
...
Application
Network

36. The Instance State (ALIVE,
NOT_ALIVE_DISPOSED,
NOT_ALIVE_NO_WRITERS) allows to
track the life-cycle transitions of the
instance to which a sample belongs
State-Based Selection
DataReader Cache
DataReader
...
State Selector
DataReader Cache
DataReader
...
Application
Network

37. Selector Example
// == ISO C++ DDS API ==
auto data =
dr.select()
.content(query)
.state(data_state)
.instance(handle)
.read();

38. your first
vortex app

39. CopyrightPrismTech,2014
Anatomy of a DDS Application

40. CopyrightPrismTech,2015
Writing Data in C++
#include <dds.hpp>
int main(int, char**) {
DomainParticipant dp(0);
Topic<Meter> topic(“SmartMeter”);
auto pqos = dp.get_default_pubqos()
<< Partition (“FR:IdF:Essonne:Gif:Essonne:Mnts:123”);
Publisher pub(dp, pqos);
DataWriter<Meter> dw(pub, topic);
while (!done) {
auto value = readMeter()
dw.write(value);
std::this_thread::sleep_for(SAMPLING_PERIOD);
}
return 0;
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

41. CopyrightPrismTech,2015
Reading Data in C++
#include <dds.hpp>
int main(int, char**) {
DomainParticipant dp(0);
Topic<Meter> topic(”SmartMeter”);
Subscriber sub(dp);
DataReader<Meter> dr(dp, topic);
LambdaDataReaderListener<DataReader<Meter>> lst;
lst.data_available = [](DataReader<Meter>& dr) {
auto samples = data.read();
std::for_each(samples.begin(), samples.end(), [](Sample<Meter>& sample) {
std::cout << sample.data() << std::endl;
}
}
dr.listener(lst);
// Print incoming data up to when the user does a Ctrl-C
std::this_thread::join();
return 0;
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

42. CopyrightPrismTech,2014
Writing Data in Scala
import dds._
import dds.prelude._
import dds.config.DefaultEntities._
object SmartMeter {
def main(args: Array[String]): Unit = {
val topic = Topic[Meter](“SmartMeter”)
val dw = DataWriter[Meter](topic)
while (!done) {
val meter = readMeter()
dw.write(meter)
Thread.sleep(SAMPLING_PERIOD)
}
}
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

43. CopyrightPrismTech,2014
Reading Data in Scala
import dds._
import dds.prelude._
import dds.config.DefaultEntities._
object SmartMeterLog {
def main(args: Array[String]): Unit = {
val topic = Topic[Meter](“SmartMeter”)
val dr = DataReader[Meter](topic)
dr listen {
case DataAvailable(_) => dr.read.foreach(println)
}
}
}
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

44. CopyrightPrismTech,2015
Writing Data in Python
import dds
import time
if __name__ == '__main__':
topic = dds.Topic("SmartMeter", "Meter")
dw = dds.Writer(topic)
while True:
m = readMeter()
dw.write(m)
time.sleep(0.1)
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

45. CopyrightPrismTech,2015
Reading Data in Python
import dds
import sys
def readData(dr):
samples = dds.range(dr.read())
for s in samples:
sys.stdout.write(str(s.getData()))
if __name__ == '__main__':
t = dds.Topic("SmartMeter", "Meter")
dr = dds.Reader(t)
dr.onDataAvailable = readData
enum UtilityKind {
ELECTRICITY,
GAS,
WATER
};
struct Meter {
string sn;
UtilityKind utility;
float reading;
float error;
};
#pragma keylist Meter sn

46. Vortex
Technology Stack

47. Device implementations
optimised for OT, IT and
consumer platforms
Native support for Cloud and
Fog Computing Architectures
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

48. Available across IT, Consumer
and OT platforms Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

49. Polyglot and Interoperable
across Programming
Languages
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

50. Fully Independent of the
Cloud Infrastructure
Private Clouds
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

51. Native Integration with the
hottest real-time analytics
platforms and CEP Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

52. Integration with mainstream
Dashboard Technologies
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

53. High
Performance
30 μs peer-to-peer latency
4+ Mmsgs/sec p2p throughput
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

54. >10 μs fog/cloud routing
latency
High
Performance Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

55. Secure
Data-Level security with
Pluggable Authentication
Access Control and Crypto
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

56. Default Plug-ins
X.509 Public Key
Infrastructure (PKI) based
authentication
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

57. Default Plug-ins
Access Control List file
signed by a shared
Certificate Authority for
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

58. Default Plug-ins
Crypto based on TLS
Cipher Suite
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

59. Secure
Data-Security as opposed
to simply Transport-Level
security
Arthur Dent
Arthur Dent
Ford Prefect
Zaphod Beeblebrox
Marvin
Trillian
left/A(r,w), left/B(r)
left/A(r,w), left/B(r,w), left/X(r)
left/*(r,w)
left/*(r), right/(w)
left/A(r,w), left/B(r,w), right/C(r,w)
Ford Prefect
Zaphod Beeblebrox
Trillian
Marvin
A
B
A,B
X
*
*
A,B,C
Identity Access Rights
Sessions are authenticated
and communication is
encrypted
Only the Topic included as
part of the access rights are
visible and accessible

60. Secure
Fine-grained access control
over Partition/Topic/
Instance regular expressions
Arthur Dent
Arthur Dent
Ford Prefect
Zaphod Beeblebrox
Marvin
Trillian
left/A(r,w), left/B(r)
left/A(r,w), left/B(r,w), left/X(r)
left/*(r,w)
left/*(r), right/(w)
left/A(r,w), left/B(r,w), right/C(r,w)
Ford Prefect
Zaphod Beeblebrox
Trillian
Marvin
A
B
A,B
X
*
*
A,B,C
Identity Access Rights
Sessions are authenticated
and communication is
encrypted
Only the Topic included as
part of the access rights are
visible and accessible

61. Secure
Support for Symmetric and
Asymmetric Authentication
Arthur Dent
Arthur Dent
Ford Prefect
Zaphod Beeblebrox
Marvin
Trillian
left/A(r,w), left/B(r)
left/A(r,w), left/B(r,w), left/X(r)
left/*(r,w)
left/*(r), right/(w)
left/A(r,w), left/B(r,w), right/C(r,w)
Ford Prefect
Zaphod Beeblebrox
Trillian
Marvin
A
B
A,B
X
*
*
A,B,C
Identity Access Rights
Sessions are authenticated
and communication is
encrypted
Only the Topic included as
part of the access rights are
visible and accessible

62. Boundary Security
Boundary security support is
enabled by Vortex-Fog
Device-to-Cloud
Communication
Peer-to-Peer
(Broker-less)
Device-to-Device
Communication
Fog Computing Fog Computing
Fog Computing
TLS
TLS

63. Boundary Security
Separates security concerns at
different scales and controls
what information is exposed
Device-to-Cloud
Communication
Peer-to-Peer
(Broker-less)
Device-to-Device
Communication
Fog Computing Fog Computing
Fog Computing
TLS
TLS

64. Typical
Deployment

65. Vortex device, such as Lite, Café,
and Web are used to share data
between different kinds of
applications within a car
Café and Web are typically used
Android / HTML5 based
infotainment
Lite in typically used in ECU,
sensors and onboard analytics

66. Vortex Fog is used to
transparently (for in car apps)
decouple and control the data
sharing within and across the
car
Vortex Fog also helps defining
security boundaries and
policies

67. Vortex Fog efficiently and
securely deals with car to car
communication
Vortex Fog allows to
decouple the transport used
for in-car communication
and that used for car-to-car
communication

68. Vortex Fog efficiently and
securely deals with cloud
connectivity adapting traffic
flows and protocols
Device-to-Cloud
Communication
Cloud Analytics
Fog Analytics
Fog Analytics
Fog Analytics
Vortex Cloud efficiently
and securely makes data
available to any device at an
Internet Scale

69. Vortex Cloud efficiently
and securely makes data
available to any device at
an Internet Scale
Device-to-Cloud
Communication
Cloud Analytics
Fog Analytics
Fog Analytics
Fog Analytics

70. Use Cases

71. CopyrightPrismTech,2015
Connected Boulevard — Nice, France
Vortex enables is the data sharing platform
for the City Operating System
Tens of thousands of devices
connected
by Vortex

72. Autonomous
Vehicles
coordination of fast moving
autonomous vehicles
intermittent
connectivity
dynamic pairing of devices

73. CopyrightPrismTech,2014
Smart Factory
0.5 TB of data
produced per day

74. CopyrightPrismTech,2014
Smart-Grid
20ms deadline for phase
alignment data

75. ESA
Extremely Large & SMART
Telescope (ELT)
1750 computing nodes
100.000 mirrors
whose position is
adjusted 100 times
per second!
ELT will allow astronomers
to probe the earliest stages
of the formation of
planetary systems and to
detect water and organic
molecules in proto-
planetary discs around
stars in the making

76. Launch System
80K+ data points with aggregate updates
rate of ~400K msgs/sec

77. Connected Aircrafts
10 TB of data every
30m of flight

78. Oil Rig 30000 data points
only 1% of available data
used today

79. Smart Lightbulbs
96Kbytes Memory
Connected
Medical Devices
12 msec period for
pleath data
Peer-to-Peer
connectivity

80. Vortex enables Smart, Secure
and Efficient Data Sharing in IoT
In
Summary

81. CopyrightPrismTech,2015