Whilst there isn’t a universal agreement on what exactly is IoT, nor on the line that separates Consumer and Industrial IoT, everyone unanimously agrees that unconstrained access to data is the game changing dimension of IoT. Vortex positions as the best data sharing platform for IoT enabling data to flow unconstrained across devices and at any scale. This presentation, will demonstrate how quickly and effectively you can build real-world IoT applications that scale using Vortex and the Intel Edison Starter Kit. Specifically, you will learn how to leverage vortex to virtualise devices, integrate different protocols, flexibly execute analytics where it makes the most sense and leverage Cloud as well as Fog computing architectures. Throughout the webcast we will leverage Intel’s Edison starter kit, available at https://software.intel.com/en-us/iot/hardware/edison, you will be able to download our code examples before the webcast to particulate to the live demo!

1. Building IoT Apps with Vortex and
the Intel Edison Starter Kit
Angelo Corsaro, PhD
Chief Technology Officer
angelo.corsaro@prismtech.com

2. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryIoT Architectures

3. CopyrightPrismTech,2015

4. CopyrightPrismTech,2015
Cloud
Cloud Centric Architectures

5. CopyrightPrismTech,2015
Cloud
Fog
Fog
Fog
Fog

6. CopyrightPrismTech,2015

7. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryVortex Intro

8. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary
Vortex is a standard technology for
efficient, ubiquitous, interoperable,
secure and platform independent data
sharing across network connected
devices
in151
Characters

9. 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
Virtualised Data
Space

10. Temporal Decoupling
Four different levels of temporal
decoupling allow to control the
trade-off between full temporal
decoupling, performances and
resource usage
t
Source
t
t
Sink
Sink
Volatile Durability
t
Source
t
t
Sink
Sink
Transient Durability

11. 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

12. Failure Detection
Vortex provides mechanism for
detecting traditional crashes as
well as performance failures
Source
tSink
Fault Notification
TD
Source
t
Sink
Performance Failure Notification
P
t
P P

13. Fault Masking
A built-in fault-masking
mechanism transparently
controls and switches between
sources of data
Source
tSink
Source t

14. Fault Masking
Vortex provides mechanism for
detecting traditional crashes as
well as performance failures
Source
tSink
Source t

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary
Topic Types
— Language Independent Declaration—

22. 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

23. 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

24. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary
Topic Types
— Language Dependent Declaration—

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

26. 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#

27. 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

28. Vortex “knows”
about application
data types and
uses this
information
provide type-
safety and
content-based
routing
Content
Awareness 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

29. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryProgramming Model

30. CopyrightPrismTech,2014
Anatomy of a DDS Application

31. 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

32. 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

33. 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

34. CopyrightPrismTech,2014
Reading Data in Scala
import dds._
import dds.prelude._
import dds.config.DefaultEntities._
object ChatLog {
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

35. CopyrightPrismTech,2014
Writing Data in C++
#include <dds.hpp>
int main(int, char**) {
DomainParticipant dp(0);
Topic<Meter> topic(“SmartMeter”);
Publisher pub(dp);
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

36. CopyrightPrismTech,2014
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

37. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryModeling Idioms

38. A state that is periodically updated
Examples are the reading of a sensor
(e.g. Temperature Sensor), the
position of a vehicle, etc.
Soft State
Reliability => BestEffort
Durability => Volatile
History => KeepLast(n)
Deadline => updatePeriod
LatencyBudget => updatePeriod/3
DestinationOrder => SourceTimestamp
// Vortex’s Scala API provides first class implementation of the
// soft-state pattern
val temperature = SoftState[AnalogSensor](topicName)

39. A state that is sporadically updated
and that often has temporal
persistence requirements
Examples are system configuration, a
price estimate, etc.
Hard State
Reliability => Reliable
Durability => Transient | Persistent
History => KeepLast(n) (often n =1)
DestinationOrder => SourceTimestamp
// Vortex’s Scala API provides first class implementation of the
// hard-state pattern. The state is transient by default and can be controlled
// by an additional argument
val runningMicrosvc = HardState[RunningMicrosvc](topicName)

40. The occurrence of something
noteworthy for our system
Examples are a collision alert, the
temperature beyond a given
threshold, etc.
Events
Reliability => Reliable
Durability => any
History => KeepAll
DestinationOrder => SourceTimestamp
// Vortex’s Scala API provides first class implementation of the
// hard-state pattern. The state is transient by default and can be controlled
// by an additional argument
val nodeError = Event[NodeError](topicName)

41. Data-centric design leverage the
same principle of Feedback-control
loops to assert a state
In other terms, the desired state is
asserted by writing a topic and the
actual state is monitored.
A control action is taken when the
desired and the actual state differ
Feedback Control Loop

42. Data-centric design leverage the
same principle of Feedback-control
loops to assert a state
In other terms, the desired state is
asserted by writing a topic and the
actual state is monitored.
A control action is taken when the
desired and the actual state differ
Feedback Control Loop Hard State
Soft State
microservice

43. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryVortex Technology Stack

44. 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

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

46. 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

47. 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

48. Integration with the popular
Node-RED framework
Device-2-DeviceDevice-2-Cloud
Fog-2-Cloud
Device-2-Fog
Cloud-2-Cloud
Fog-2-Fog
infrastructuresdk

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

50. 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

51. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryIntel Edison Starter Kit

52. Edison is an Octo-Linux based x86
board for prototyping IoT
applications
Edison shirts an Intel optimised
OpenJDK implementation — perfect
for Café
The Grove Starter Kit Plus provides a
set of nice sensor, displays and
actuators to get started with IoT
IoT Starter Kit

53. Before getting started with playing
with the demo, make sure your
board is properly configured
You can check the configuration
using the configure_edison
command
Ensure that the WiFi is working!
Configuring your Board

54. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiarySensors

56. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryDisplays

57. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary

58. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryLED

59. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary

60. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryProgramming

61. Intel has contributed several Open
Source project that provide high
level abstractions to access Sensors,
LCD, Led, etc. , for a very large
number of different kinds of sensors
and manufacturers
Programming

62. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary
LED
Light Sensor

63. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary
IoT Applications
with Vortex and Edison

64. CopyrightPrismTech,2015
Cloud
Fog
Fog
Fog
Fog

65. CopyrightPrismTech,2015

66. Vortex can be used to virtualise
sensors/displays/motors/.. and
make them universally available
Instead of locally programming
sensors/displays/motors we
abstracts them with Topics and
access /control through DDS in a
location transparent manner
Virtualisation
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

67. The agentv micro service
framework is used to flexibly
provision and manage applications
Deployment
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

68. struct AnalogSensor {
short id;
float rvalue;
float value;
};
#pragma keylist AnalogSensor id
Temperature Sensor
Topic
AnalogSensor
Temperature
SoftState
sensor specific microsvc
write
read
read
read

69. struct LCDText {
short id;
unsigned short row;
unsigned short col;
string text;
};
#pragma keylist LCDText id
LCD
Topic
LCDText
LCDText
HardState
sensor specific microsvc
read
read
struct LCDColor {
short id;
short r;
short g;
short b;
};
#pragma keylist LCDColor id
Topic
LCDColor
LCDColor
HardState
write
write
write

70. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiaryCoding-Lab

71. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary

72. This slides have been crafted by Angelo Corsaro
Any use of these slides that does include me as Author/Co-Author is plagiary

73. Vortex makes it extremely easy
to build IoT applications
Get started Building IoT apps
with Vortex and Edison!
In
Summary

74. CopyrightPrismTech,2015