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.
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