smart, secure and efficient data sharing in iot

Smart, Secure and Efficient Data Sharing in IoT

Angelo Corsaro, PhD Chief Technology Officer

angelo.corsaro@prismtech.com

HYPE CYCLE 2015

GARTNER

HYPE CYCLE 2015

GARTNER

HYPE CYCLE 2015

GARTNER

What is IoT all About?

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

smartcollar

connected f0rk

smart socks

Smart Lightbulbs

the ValueIIoT

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

Smart Factory0.5 TB of data

produced per day

Autonomous Vehicles

coordination of fast moving autonomous vehicles

intermittent connectivity

dynamic pairing of devices

Smart-Grid20ms deadline for phase

alignment data

Interoperability

Oil Rig 30000 data pointsonly 1% of available data

used today

Smart Lightbulbs96Kbytes Memory

Connected Medical Devices

12 msec period for pleath data

Peer-to-Peer connectivity

smart cities

Connected Aircrafts

10 TB of data every 30m of flight

Launch System80K+ 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

CIoT / IIoT Differences

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

Data SharingIIoT

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.

produced per day

Device-2-Devicecommunication

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

produced per day

Cloud + FogComputing

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

Autonomous Vehicles

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

used today

Interoperability

Data sharing standard are a pre-prerequisite for IoT.

Without standards there is not interoperability, without interoperability there is not IoT

alignment data

Interoperability

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

The (I)IoT Data SharingDDS

Standard

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

DDS in131 Characters

The DDS Standard

Standard

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

4DDS is independent from the

- Programming language,

- Operating System

- HW architecture

Platform Independent

Grasping the Idea

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 Reader

Data Writer

TopicAQoS

TopicBQoS

TopicCQoS

TopicDQoS

Conceptual Model

Data Writer

Data Reader

Data Writer

TopicAQoS

TopicBQoS

TopicCQoS

TopicDQoS

Conceptual Model Actual Implementation

Data Writer

Data Reader

Data Writer

TopicAQoS

TopicBQoS

TopicCQoS

TopicDQoS

TopicAQoS

Data Writer

Data Reader

Data Writer

TopicAQoS

TopicBQoS

TopicCQoS

TopicDQoS

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 Reader

Data Writer

TopicAQoS

TopicBQoS

TopicCQoS

TopicDQoS

TopicAQoS

The communication between the DataWriter and matching DataReaders can be “brokered” but still exploiting UDP/IP (Unicast and Multicast)or TCP/IP

Abstracting Connectivity

Cloud Computing

Fog Computing

Device-to-Cloud Communication

Device-to-Device Communication

Fog-to-Cloud Communication

Cloud-to-Cloud Communication

Device-to-Device Communication

Collect | Store | Analyse | Share

Fog Computing

Autonomous Vehicles

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.

TopicTypeName

struct TemperatureSensor { @key long sid; float temp; float hum; }

5DDS “knows” about application data types and uses this information provide type-safety and content-based routing

Content Awarenessstruct TemperatureSensor { @key long sid; float temp; float hum; }

sid temp hum101 25.3 0.6507 33.2 0.7913 27,5 0.551307 26.2 0.67

“temp > 26 AND hum >= 0.6”

sid temp hum

507 33.2 0.71307 26.2 0.67

TempSensor

used today

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 ServiceDomain

Participant

DURABILITY

OWENERSHIP

DEADLINE

LATENCY BUDGET

LIVELINESS

RELIABILITY

DEST. ORDER

Publisher

DataWriter

PARTITION

DataReader

Subscriber

DomainParticipant

offered QoS

Topicwrites reads

Domain Idjoins joins

produces-in consumes-from

RxO QoS Policies

requested QoS

alignment data

Interoperability

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

Ford Prerfect

Zaphod Beeblebrox

Marvin

Trillian

A(r,w), B(r)

A(r,w), B(r,w), X(r)

*(r,w)

A(r,w), B(r,w), C(r,w)

Ford Prerfect

Zaphod Beeblebrox

Trillian

Marvin

Identity Access RightsSessions 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

Your First DDS App!

Anatomy of a DDS Application

Writing Data in Python

import dds import timeif __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 Pythonimport ddsimport 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, secure and efficient data sharing in iot

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