overview: data-driven iot™ platform

Overview: Data-driven IoT™ Platform

Copyright © 2014 Oleg Puzanov. All rights reserved.

About Us: Project Team

• M2M and IoT Technologist, Software and Hardware Geek • Director, Software Engineering @ Cogniance: www.cogniance.com • IoT Primer blog: www.iotprimer.com • 12+ years in ICT domain projects, large experience with US and

EMEA markets for embedded, web and cloud projects.

Oleg Puzanov

• Senior Expert in embedded systems, FPGA/ASIC design • Senior Systems Engineer @ Cogniance: www.cogniance.com • Founder and Managing Director @ Unicore Systems:

www.unicore.co.ua

Oleg Uzenkov


http://www.cogniance.com

http://www.iotprimer.com

http://www.cogniance.com

http://www.unicore.co.ua

Introduction


Concept: Data-driven IoT™ Platform


Vertical Applications

Connected Fitness/Telecare NGN Telecom Smart Grid and Utilities

Smart ParkingConnected Car/Vehicles Smart Agriculture

Connected Retail Smart Home/Energy Environmental Safety


High-level Architecture

IoT-ACUI

IoT-SCR

IoT-GW

IoT-SCR

IoT-MD

IoT-FGF

IoT-DPF

IoT-DPF

IoT-SCR

IoT-MD

Location Field Networks

RFID WSAN

BluetoothWLAN IP WAN

XMPP

MQTT

XMPP

MQTT

IoT-DPF

IoT-BIG

IoT-WEB


Prototyping Setup: IoT-GW Device v0.1

BeagleBone Black with Android 4.2.2 (reduced)

433 MHz eZ430 Dongle with DASH7

433 MHz TI Chronos Watch with DASH7

2.4 GHz CC2531 Dongle with TI Z-StackCC2531 and

XBee-Pro

DASH7

ZigBee

USB

USB


Problem-Solution Statement


Problem Statement

Poorly specified in chunks across standards • OGC SWE, W3C SSN, IoT-A Reference Architecture,

ETSI M2M - all of them cover some details in a partial way.

• No single complete standard on the state of today!

Poorly addressed by IoT solution vendors • Industry is focused on device-to-cloud connectivity and

Big Data Analytics instead.• IoT industry is young - many gaps and ambiguities,

vendors have hard times in understanding and adopting IoT architectures.

Disconnect between horizontal platforms and vertical applications • “Everything Connected” or “Device Cloud” horizontal

platforms - most of them are too much generic and don’t support the application-level specifics.

• Data models and data protocols - not defined and not implemented in the horizontal platforms.

No solutions combining all critical characteristics • Semantic, spatial, contextual, distributed and managed

IoT Data Layer - any products supporting all of these characteristics today?

• Offline mode and smart data synchronization are very poorly supported - many IoT products are limited to “always on” device-to-cloud integration.

Challenging E2E integration of IoT applications • Heterogenous technologies, data sources and

connectivity interfaces.• Lack of standards and well-established practices for

IoT application architectures.• Legacy approach for application data models and data

integration (or no approaches at all).• Slow IoT adoption across verticals, both B2C and B2B

applications.

IoT paradigms are not adopted, benefits and innovations are not delivered • M2M, SCADA, RFID and WSAN applications have

been around for decades - many IoT applications do not bring anything new into this space.

• User experiences - they’re still far away from the main ideas of IoT. Legacy UI and user-facing features “hide” all innovations of IoT applications.

Lack of system-wide intelligence and smart data processing • “Dumb data pipes” - sending data from sensor

networks to the cloud applications.• Context awareness - still in the early stages for IoT

applications, including contextual data synchronization and personalized context-driven UI.

• Cloud-side intelligence mainly - role of IoT field networks is limited to data acquisition.

IoT Application LayerIoT Data Layer


Solution

Successful IoT = Data-driven Implementation

1

2

3

Data models are defined and implemented.

E2E integration is driven by IoT Data Layer, not technologies.

Enabled system-wide intelligence and smart data processing for field networks, cloud platforms and user interfaces.


Platform Details


Augmented Context UI Application (IoT-ACUI)

IoT network view with Augmented Reality features

Android and iOS: tablets, smartphones

Contextual POI presentation in real-time (RFID, WSAN)

Rich spatial data models

Connects to IoT-GW via Bluetooth or directly to the cloud

GeoJSON over XMPP or MQTT for data synchronization

Offline mode by default: local cache of IoT-SCR


IoT Gateway Device (IoT-GW)

• Integrated firmware to run on ARM Cortex-A or Intel x86/64 platforms.

• Linux v3.8.x or Android 4.2.x

• Built on top of OSGi and Java frameworks, easy porting to other operating systems.

Wearables

Home Gateways

In-vehicle Gateways

Body Area Network Gateways

Industrial Gateways

Smart Metering/Utility Gateways

Telecare/Telehealth Gateways

• Includes IoT-SCR, IoT-MD, IoT-FGF, IoT-DPF and system specific modules.


Field Gateway Framework (IoT-FGF)

OSGi bundles running on Embedded Android/Linux stack

RFID, RTLS, Proximity and WSAN controllers

DASH7, ZigBee, Bluetooth, Wi-Fi and OBDII supported initially

Part of IoT-GW responsible for field connectivity and services

No protocol stack implementations, integration only (e.g. TI CC2530 with Z-Stack)

6LoWPAN, Wireless M-Bus, WirelessHART, KNX in the roadmap


IoT Vehicle Gateway Device (based on IoT-GW)

Heavy-duty Trucks

Civil Cars

Industrial Machinery

Agricultural Equipment

Military Vehicles

Construction Equipment


Smart Context Repository (IoT-SCR)

Full Repository Lite Repository

Spatial Search GeoSPARQL

CRUD Batch

Core Repository Functions

Context Processing Functions

Export/Import FunctionsSemantic Spatial Contextual

• Geospatial RDF framework with advanced context-driven data processing features.

GeoJSON GeoRSSRDF

• Graph DB (Neo4j) is used by default for storage engine with H2 DB for Lite version.


Metadata Directory (IoT-MD)

Core Metadata Field Services Metadata

Cloud Services Metadata

Application Metadata

• Hierarchical data model definitions - ontologies, class hierarchy, templates • Covers the common classes and the application-specific data models • RDF/RDFS, OWL, GeoJSON or the native POJO classes • Import/Export into Java (POJO) data models • Every supported vertical application has its own metadata


Data Protocol Framework (IoT-DPF)

Communication Protocols

XMPP MQTT HTTP

Payload Formats

GeoJSON GeoRSS RDF

Data Transfer Functions

Context Sync

PubSub

Data Query

Upload/DownloadCommunication framework for data synchronization and event-driven processing.


Big Data Integration Framework (IoT-BIG)

• Enables Apache Hadoop and Apache Storm for IoT-SCR with all related tooling for Big Data management.

!• Both batch processing and real-time

processing of Big Data.

Real-time Processing Batch Processing Storage Analytics

Storm


Web Portal Demo (IoT-WEB)

API for Web Apps (REST, Java)

Integrated Portlets Framework

+

Integration Middleware

Demo web portal and API to showcase IoT applications development.


Roadmap for Industry Standards and Features


Out of Scope for Data-driven IoT™ Platform

• Field protocol stack implementations (ZigBee, DASH7, 6LoWPAN) - integration with 3rd-party SDK and API only.

• Remote device management - to be handled partially by OSGi remote management functions and commands over XMPP/MQTT protocols.

• Complete web portal UI - demo web portal is provided only. Reference implementation to showcase the platform features.

• Full-featured Big Data analytics and reporting - current implementation relies on the available tooling for Apache Hadoop and Apache Storm.

• On-device firmware for RFID tags or Sensor nodes - we use OpenTag, Contiki OS, Tiny OS, FreeRTOS, TI Z-Stack or other ready-to-use software stacks here. Minor configuration changes are applied only, no major implementations of tag/sensor software.

To be focused on the main scope of Data-driven IoT™ platform the following features are considered out of scope or low priority:


overview: data-driven iot™ platform

Technology