Published on February 17, 2014
Copyright © 2014 Oleg Puzanov. All rights reserved. Overview: Data-driven IoT™ Platform
Copyright © 2014 Oleg Puzanov. All rights reserved. About Us: Project Team Oleg Puzanov • • • • 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 Uzenkov • • • 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
Copyright © 2014 Oleg Puzanov. All rights reserved. Introduction
Copyright © 2014 Oleg Puzanov. All rights reserved. Concept: Data-driven IoT™ Platform
Copyright © 2014 Oleg Puzanov. All rights reserved. Vertical Applications Connected Fitness/Telecare NGN Telecom Smart Grid and Utilities Connected Car/Vehicles Smart Parking Smart Agriculture Connected Retail Smart Home/Energy Environmental Safety
Copyright © 2014 Oleg Puzanov. All rights reserved. High-level Architecture Location RFID Field Networks WSAN Bluetooth WLAN XMPP IoT-ACUI IP WAN XMPP MQTT IoT-GW MQTT IoT-DPF IoT-SCR IoT-SCR IoT-SCR IoT-DPF IoT-MD IoT-MD IoT-FGF IoT-BIG IoT-DPF IoT-WEB
Copyright © 2014 Oleg Puzanov. All rights reserved. Prototyping Setup: IoT-GW Device v0.1 433 MHz TI Chronos Watch with DASH7 DASH7 433 MHz eZ430 Dongle with DASH7 USB BeagleBone Black with Android 4.2.2 (reduced) USB CC2531 and XBee-Pro ZigBee 2.4 GHz CC2531 Dongle with TI Z-Stack
Copyright © 2014 Oleg Puzanov. All rights reserved. Problem-Solution Statement
Copyright © 2014 Oleg Puzanov. All rights reserved. Problem Statement IoT Data Layer IoT Application Layer 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. 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. 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! 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. 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. 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 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.
Copyright © 2014 Oleg Puzanov. All rights reserved. Solution Successful IoT = Data-driven Implementation 1 Data models are deﬁned and implemented. 2 E2E integration is driven by IoT Data Layer, not technologies. 3 Enabled system-wide intelligence and smart data processing for ﬁeld networks, cloud platforms and user interfaces.
Copyright © 2014 Oleg Puzanov. All rights reserved. Platform Details
Copyright © 2014 Oleg Puzanov. All rights reserved. Augmented Context UI Application (IoT-ACUI) IoT network view with Augmented Reality features 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 Android and iOS: tablets, smartphones
Copyright © 2014 Oleg Puzanov. All rights reserved. IoT Gateway Device (IoT-GW) Home Gateways Wearables Body Area Network Gateways In-vehicle Gateways Telecare/Telehealth Gateways Smart Metering/Utility Gateways • Integrated ﬁrmware to run on ARM Cortex-A or Intel x86/64 platforms. • Includes IoT-SCR, IoT-MD, IoT-FGF, IoT-DPF and system speciﬁc modules. • Linux v3.8.x or Android 4.2.x • Built on top of OSGi and Java frameworks, easy porting to other operating systems. Industrial Gateways
Copyright © 2014 Oleg Puzanov. All rights reserved. Field Gateway Framework (IoT-FGF) Part of IoT-GW responsible for ﬁeld connectivity and services OSGi bundles running on Embedded Android/Linux stack RFID, RTLS, Proximity and WSAN controllers DASH7, ZigBee, Bluetooth, Wi-Fi and OBDII supported initially 6LoWPAN, Wireless M-Bus, WirelessHART, KNX in the roadmap No protocol stack implementations, integration only (e.g. TI CC2530 with Z-Stack)
Copyright © 2014 Oleg Puzanov. All rights reserved. IoT Vehicle Gateway Device (based on IoT-GW) Civil Cars Heavy-duty Trucks Industrial Machinery Construction Equipment Military Vehicles Agricultural Equipment
Copyright © 2014 Oleg Puzanov. All rights reserved. Smart Context Repository (IoT-SCR) Semantic Spatial Contextual RDF GeoJSON GeoRSS Export/Import Functions Context Processing Functions Core Repository Functions Spatial Search CRUD • Geospatial RDF framework with advanced context-driven data processing features. • Graph DB (Neo4j) is used by default for storage engine with H2 DB for Lite version. GeoSPARQL Batch Full Repository Lite Repository
Copyright © 2014 Oleg Puzanov. All rights reserved. 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
Copyright © 2014 Oleg Puzanov. All rights reserved. Data Protocol Framework (IoT-DPF) XMPP MQTT HTTP Communication Protocols GeoJSON GeoRSS RDF Payload Formats Context Sync Communication framework for data synchronization and event-driven processing. Data Query PubSub Upload/Download Data Transfer Functions
Copyright © 2014 Oleg Puzanov. All rights reserved. Big Data Integration Framework (IoT-BIG) Storm • ! • Real-time Processing Batch Processing 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. Storage Analytics
Copyright © 2014 Oleg Puzanov. All rights reserved. Web Portal Demo (IoT-WEB) API for Web Apps (REST, Java) Integration Middleware + Integrated Portlets Framework Demo web portal and API to showcase IoT applications development.
Copyright © 2014 Oleg Puzanov. All rights reserved. Roadmap for Industry Standards and Features
Copyright © 2014 Oleg Puzanov. All rights reserved. Out of Scope for Data-driven IoT™ Platform To be focused on the main scope of Data-driven IoT™ platform the following features are considered out of scope or low priority: • • • • • 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.
Overview: Data-driven IoT™ Platform Nov 29, 2014 Technology oleg-puzanov
... with Internet of Things (IoT) and machine ... your core business with data-driven intelligence ... Internet of Things (IIoT) platform based ...
Discover how Oracle IoT ... Overview; Oracle Internet of ... Gain new data-driven insights and drive actions from Internet of Things (IoT) ...
World's most economical and scalable could based IoT Platform. ... Osmosis Platform Product Overview. ... triggers and alarms using our meta data driven ...
Analyses of the IoT platform market based on different parameters such ... and developed data-driven research for its global IT research ... OVERVIEW: 9: $ ...
Internet of Things. ... Customers; Simplify IoT. Gain new data-driven insights and drive actions ... Enterprise-grade platform for mission critical IoT ...
... and operation of next-generation IoT applications that unlock data-driven insights ... to join the C3 IoT Platform ... IoT Company Overview.
Get Started Try IBM Watson IoT Platform. ... IBM Watson IoT partners with Local Motors to transform mass transportation using cognitive commuting ...