Smart Data Sharing in IoT with DDS

Smart, Secure and Efficient
Data Sharing in IoT
Angelo
Corsaro,
PhD

Chief
Technology
Officer

angelo.corsaro@prismtech.com

IoT is about extracting value through the
insights derived from the real-time and historical
data produced by a cyber-physical system
— Data is the currency of IoT —

the buzZ
digital humanism
CIoT

While consumer applications such as ﬁtness
monitors and self-driving cars attract the most
attention and can create signiﬁcant value, we estimate
that B2B/Industrial applications can generate nearly
70 percent of potential value enabled by IoT.  
THE INTERNET OF THINGS:
MAPPING THE VALUE BEYOND THE HYPE
Mc Kinsey, June 2015

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

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

Smart-Grid
20ms deadline for phase
alignment data
Interoperability

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

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

Connected Aircrafts
10 TB of data every
30m of ﬂight

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

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

IIoT is concerned with reactive cyber-physical systems
IIoT is about interacting with the physical world

Cloud-centric architectures centred around
device-to-cloud communication are not
applicable/suﬀicient for IIoT applications
because of performance, connectivity and
resource constraints

This subtle but essential diﬀerence
introduces a series of requirements for
IIoT platform that are not addressed by
device-2-cloud centric IoT platforms

Location
Transparency
Data should ﬂow where needed
transparently and independently
from the location its source so to
allow for analytics to be deployed/
migrate where it makes the most
sense, i.e. edge, cloud, etc.

Device-2-Device
communication
Device-2-Cloud connectivity is not
always possible due to connectivity
challenges, response time or data
volumes

Cloud + Fog
Computing
Cloud and Fog computing
architectures should be
transparently supported to allow for
data to be processed wherever
makes the most sense

Durability
Along with real-time data,
historical data should be available
for query and non-real-time analytics

Interoperability
Data sharing standard are a pre-
prerequisite for IoT.
Without standards there is not
interoperability, without
interoperability there is not IoT

Security
Data-Level security should be
provided to simplify the deployment
of secure IoT systems

The (I)IoT Data Sharing
DDS
Standard

DDS is a standard technology for
eﬀicient, ubiquitous, interoperable,
secure, and platform independent data
sharing across network connected
devices
DDS in131
Characters

Proven in Defence / Aerospace
Integrated Modular Vetronics Training & Simulation Systems Naval Combat Systems
Air Traffic Control & Management Unmanned Air Vehicles Aerospace Applications

Broad Commercial Applications
Agricultural Vehicle Systems
Train Control Systems Complex Medical Devices
Smart CitiesLarge Scale SCADA Systems
High Frequency Auto-Trading

DDS is independent from the
- Programming language,
- Operating System
- HW architecture
Platform Independent

DDS provides a Distributed Data
Space abstraction where
applications can autonomously
and asynchronously read and
write data enjoying spatial and
temporal decoupling
Its built-in dynamic discovery
isolates applications from
network topology and
connectivity details
DDS’ Data Space is decentralised
High Level Abstraction
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

Conceptual Model
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS

Conceptual Model Actual Implementation
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
...
Data
Writer
Data
Writer
Data
Writer
Data
Reader
Data
Reader
Data
Reader
Data
Reader
Data
Writer
TopicA
QoS
TopicB
QoS
TopicC
QoS
TopicD
QoS

The
communication
between

the
DataWriter
and
matching

DataReaders
can
be
peer-‐to-‐
peer
exploiting
UDP/IP

(Unicast
and
Multicast)or

TCP/IP
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

“brokered”
but
still

exploiting
UDP/IP
(Unicast

and
Multicast)or
TCP/IP

DDS supports the deﬁnition of Data
Models.
These data models allow to
naturally represent physical and
virtual entities characterising the
application domain
DDS types are extensible and
evolvable, thus allowing incremental
updates and upgrades
Data Centricity

A Topic defines a domain-wide information’s
class
A Topic is defined by means of a (name, type,
qos) tuple, where
• name: identifies the topic within the
domain
• type: is the programming language type
associated with the topic. Types are
extensible and evolvable
• qos: is a collection of policies that express
the non-functional properties of this topic,
e.g. reliability, persistence, etc.
Topic
Topic
Type
Name
QoS
struct
TemperatureSensor
{

@key

long
sid;

float
temp;

float
hum;

}

DDS “knows” about
application data types
and uses this
information provide
type-safety and content-
based routing
Content Awareness
struct
TemperatureSensor
{

@key

long
sid;

float
temp;

float
hum;

}

sid temp hum
101 25.3 0.6
507 33.2 0.7
913 27,5 0.55
1307 26.2 0.67
“temp
>
26
AND
hum
>=
0.6”
sid temp hum
507 33.2 0.7
1307 26.2 0.67
Type
TempSensor

DDS provides a rich set of QoS-
Policies to control local as well as
end-to-end properties of data
sharing
Some QoS-Policies are matched
based on a Request vs. Oﬀered
(RxO) Model
QoS Policies
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

For data to flow from a DataWriter (DW) to
one or many DataReader (DR) a few
conditions have to apply:
The DR and DW domain participants have
to be in the same domain
The partition expression of the DR’s
Subscriber and the DW’s Publisher should
match (in terms of regular expression
match)
The QoS Policies oﬀered by the DW should
exceed or match those requested by the DR
Quality of Service
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

Support for ﬁne grained
access control
Support for Symmetric and
Asymmetric Authentication
Standard Authentication,
Access Control, Crypto, and
Logging plug-in API
Security
Arthur Dent
Arthur Dent
Ford Prerfect
Zaphod Beeblebrox
Marvin
Trillian
A(r,w), B(r)
A(r,w), B(r,w), X(r)
*(r,w)
*(r)
A(r,w), B(r,w), C(r,w)
Ford Prerfect
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

The DDS Security defines a framework for interoperable security with pluggable:
- Authentication
- Access Control
- Crypto
- Logging
- Tagging
DDS Security

Authentication
X.509 Public Key Infrastructure (PKI) with pre-configured shared Certificate Authority (CA)
Digital Signature Algorithm (DSA) with Diﬀie-Hellman and RSA for authentication and key
exchange
Access Control
Access Control List file signed by a shared Certificate Authority
Police access to join Domains and Partitions and read/write Topics as well as instances
Cryptography
Protected key distribution
AES128 and AES256 for encryption
HMAC-SHA1 and HMAC-SHA256 for MAC
Default Plugins

Anatomy of a DDS Application

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

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

DDS enables Smart, Secure
and Eﬀicient Data Sharing in IoT
In
Summary

slides available at http://bit.ly/seed-iot

Smart Data Sharing in IoT with DDS

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (20)

Similar to Smart Data Sharing in IoT with DDS

Similar to Smart Data Sharing in IoT with DDS (20)

More from Angelo Corsaro

More from Angelo Corsaro (17)

Recently uploaded

Recently uploaded (20)

Smart Data Sharing in IoT with DDS