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20180204 wf iot tutorial - small

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4hrs Tutorial at WF-IoT 2018 - Singapore, Feb 2018

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20180204 wf iot tutorial - small

  1. 1. From Research to Innovation in IoT: why is technology transfer so hard ? February 2018 IEEE WF-IOT Raffaele Giaffreda Chief IoT Scientist Twitter: @giaffred
  2. 2. outline •a layered perspective on IoT challenges •focus on some key research / business areas •turning research into concrete solutions •are we ready for business?
  3. 3. WHO AM I ?
  4. 4. • Chief IoT Scientist - CREATE-NET, Italy • 20yrs experience in the telecom domain: BT and Telecom Italia • large projects, patent holder, public speaking • >5mEur funding acquisition • IEEE IoT newsletter editor-in-chief • MSc, Telecoms Engineering, University College London, U. of London • MSc, Electronic Engineering, Optical Telecommunication Systems, Politecnico di Torino 4 About me
  5. 5. 1982
  6. 6. Information Digital World Real World of “information” 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000
  7. 7. Real World Digital World 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 what does it take? THE IOT ENABLER
  8. 8. SENSING having something to say… SENSORS COMMS EMBED’D SYSTEMS PROTOC’S DATA STRUCT’S PLATF’S
  9. 9. transistor density / space efficiency Turing’s Pilot ACE: Automatic Computing Engine TINY CHEAP LOW POWER density doubling every 2 yrs
  10. 10. sensing technology enabler Internet of *** things noisy things vehicles smelly things radioactive things underwater things nano things floating things tasty things “delle cose belle” …
  11. 11. STILL WONDERING WHY?
  12. 12. RESEARCH CHALLENGES… MEMS (Micro-Electro-Mechanical Systems) – see FBK J nanotechnology intrabody sensing for healthcare applications higher granularity in spectrum of sensed entities
  13. 13. Graphene Sensors • Single layer of carbon atoms arranged to form a two-dimensional honeycomb lattice • Graphene will enable sensors that are smaller and lighter • Graphene is thought to become especially widespread in biosensors and diagnostics. • The large surface area of graphene can enhance the surface loading of desired biomolecules, and excellent conductivity and small band gap can be beneficial for conducting electrons between biomolecules and the electrode surface. • Biosensors can be used, among other things, for the detection of a range of analytes like glucose, glutamate, cholesterol, hemoglobin and more. • Graphene-based nanoelectronic devices have also been researched for use in DNA sensors (for detecting nucleobases and nucleotides), Gas sensors (for detection of different gases), PH sensors, environmental contamination sensors, strain and pressure sensors, and more. http://www.manchester.ac.uk/discover/news/manchester-scientists-develop-graphene-sensors-that- could-revolutionise-the-internet-of-things/ https://www.graphene-info.com/graphene-sensors
  14. 14. Quantum Technologies Squeezing the area by million times ! Volume reduced by 1011 times ! Courtesy of Mher Ghulinyan (FBK, CMM)
  15. 15. 50 microns Courtesy of Mher Ghulinyan (FBK, CMM)
  16. 16. no doubt we can sense / produce digital data from our real world Real World Digital World 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011 100101101100010011 110101101010001010 100101101100010001 101001101010001010 100101101100010000 101101001010001011
  17. 17. EMBEDDED SYSTEMS (a system integrator’s perspective) giving voice to your thoughts… SENSORS COMMS EMBED’D SYSTEMS PROTOC’S DATA STRUCT’S PLATF’S
  18. 18. Cent MegaHertz KiloByte courtesy of Mattia Antonini
  19. 19. Constrained Nodes (IETF classification) Data (RAM) Code (ROM) Class 0 (Too constrained) << 10 KB << 100 KB Class 1 (Quite constrained) ~ 10 KB ~ 100 KB Class 2 (Not so constrained) ~ 50 KB ~ 250 KB courtesy of Mattia Antonini
  20. 20. IoCT OSes Features • Real-Time OS • Full IPv6 Stack • Multi-hops support • Multitasking • Power Management • Application-agnostic RTOS Kernel I/O Management Task Management Memory Management Interrupt & Event Handling Timer Management Synchronizati on & Communicatio n courtesy of Mattia Antonini
  21. 21. courtesy of Mattia Antonini
  22. 22. 6LoWPAN RPL IPv6 UDPCoAP CBOR Flexible Memory Management High resolution timersMulti-Threading Multi-platform 8 – 16 – 32 bits courtesy of Mattia Antonini
  23. 23. RESEARCH CHALLENGES • …getting more and more crammed into RTOS… • energy efficiency • size
  24. 24. COMMUNICATIONS making sure one can hear another… SENSORS COMMS PROTOC’S DATA STRUCT’S PLATF’S EMBED’D SYSTEMS
  25. 25. bandwidth / spectral efficiency com m unication density doubling every 2.5 yrs
  26. 26. The physics… • Radio signal attenuation proportional to frequency • Longer wavelength, longer range • Sub-1GHz band • robust and reliable communication with low-power budgets • bandwidth limitation • Modulation techniques • (U)NB vs. Spread Spectrum
  27. 27. wireless technologies for M2M “horses for courses…” LoRa™ Alliance White Paper © Mobile Experts, 2015 BLE – Bluetooth Low Energy LPWA – Low Power Wide Area RPMA – Random Phase Multiple Access indoor coverage, low cost, long battery life and large number of devices (>10K per AP) http://glowlabs.co/wireless-protocols/ - table comparing wireless protocols for IoT
  28. 28. trendy comms for IoT: LPWANs • LoRa (non managed) • SIGFOX (managed services) • Vodafone and Huawei (NB-IOT – 3GPP LTE standard)
  29. 29. LoRa basic features • 868 MHz • 125 KHz channel • 250 bps – typical • non-managed • star topology, thousands of nodes / gateway • LoRaWAN L2 protocol for networking (security, duplication etc.)
  30. 30. SIGFOX • UNB – 100 Hz • 12 bytes per message • 140 messages per day max (ISM bands regulation, 1% duty cycle) • 100 bps • 2-way communication • very high power efficiency • 1 Eur / year
  31. 31. NB-IoT • the telco operators’ bet • 3GPP LTE • announcement • piggybacking existing infrastructure • low-cost to deploy, wide coverage, but • subscription based, quality league • Prototypes exist but no commercial hardware / deployments yet
  32. 32. LPWAN world vendors • Semtech Corporation (California), • LORIOT (Switzerland), • NWave Technologies (London), • SIGFOX (France), • WAVIoT (Texas), • Actility (France), • Ingenu (San Diego), • Link Labs (Maryland), • Weightless SIG, and • Senet, Inc. (Portsmouth), • Other stakeholders of the Low Power Wide Area Network market include telecom operators such as Vodafone (U.K.) and Orange (France), among others who integrate these smart devices and sell them to end users to cater to their unique business requirements.
  33. 33. LPWAN • Ovum Research 2017: Five Internet of Things Trends to Watch “IoT connectivity: LPWA technologies become mainstream”
  34. 34. LoRa / NB-IoT comparison
  35. 35. and the winner is…
  36. 36. A reasonably well designed technology different spreading factors (12) for different data rates
  37. 37. LoRa WIDE COVERAGE not every human being runs as fast as Usain Bolt!
  38. 38. LoRa/LoRaWAN: Test and prototyping LoRaWAN coverage tests (Trento) Prototyping gateway LoRaWAN and monitoring stations with open hw & sw LoRaWAN Gateway PoE Waterproof Case Indoor LoRaWAN Gateway
  39. 39. LoRa/LoRaWAN: Assets
  40. 40. 5G anyone? • while LPWANs and the IoT world is going ahead at its own pace • wireless networking research focusing on • issue of latency • tactile internet scenarios • bandwidth… • but…not only radio technologies
  41. 41. • 5G is not just about speed and more flexible networks! • 5G is about having a better mobile network that can lead to improved/futuristic application smart scenarios • 5G will in fact leverage on: • Virtualised/programmable high speed dynamic access & transport networks • Decreased latency thanks to Mobile Edge/Fog computing (Tactile Internet, Enhanced Virtual Reality, Telerobotics,…) • Secure and interoperable IoT infrastructures for a huge variety of Smart Scenarios (Industry 4.0, Smart Cities, Connected Cars,…) things to remember about 5G… 42
  42. 42. RESEARCH CHALLENGES? • cheaper • energy efficient • longer range • higher bandwidth • low latency • … • some little extras (positioning)
  43. 43. PROTOCOLS don’t speak all at the same time… SENSORS PROTOC’S DATA STRUCT’S PLATF’S EMBED’D SYSTEMS COMMS
  44. 44. 6LowPAN, CoAP, MQTT etc. protocol adaptations to optimise the use of wireless, low power, limited proc power… THIS IS ABOUT GETTING THE MOST OUT OF THE COMM MEDIUM TCP to optimise use of “Best Effort Internet”… …an example from Z-Wave, home automation protocol…
  45. 45. Research Challenges efficient use of the medium M. Vecchio et al: WSNs compression schemes 5G (?) for Tactile Internet reducing latency below ms
  46. 46. DATA STRUCTURES understanding the contents… SENSORS DATA STRUCT’S PLATF’S EMBED’D SYSTEMS COMMS PROTOC’S
  47. 47. …preparing gathered data to be exploited by the application…
  48. 48. From standards to bespoke data structures • develop applications once, deploy many times • no additional coding for adding new sensors…provided they all sing from the same standard sheet • about semantic interoperability UNREAD EMAILS effort needed for archiving them largely outweighs simple searches we so much got used to these days
  49. 49. the UNCAP examplechannel channel channel channel channel stream User_ID Device_ID Timestamp Type Payload POSIT’N User_ID Device_ID Timestamp Type Payload MEASURM’S User_ID Device_ID Timestamp Type Payload ALARMS Payload "properties": { "blood_glucose": { "allOf": [ { "$ref": "#/definitions/unit_value” }, { "properties": { "unit": { "enum": [ "mg/dL", "mmol/L” ] } } } ] } i.e. location stream x,y,z channels
  50. 50. Research challenges RESEARCH CHALLENGES…
  51. 51. PLATFORMS easy learning books… SENSORS PLATF’S EMBED’D SYSTEMS COMMS PROTOC’S DATA STRUCT’S
  52. 52. , 2015
  53. 53. The IoT is dead. Long live the IoT
  54. 54. what is a platform? • a comprehensive (software) offer of services that puts together a mix of what presented so far • main purpose for IoT platforms is to provide more or less automated features that help easily create applications that exploit data for a purpose • enable you to innovate without worrying about the details • fast implementation, testing, validation, delivery cycles • yet, n-dimensional choice
  55. 55. In the case of IoT a platform will consist of… source: IoT Analytics
  56. 56. doi:10.1016/j.comcom.2016.03.015 …this is also where it starts to get more crowded! Open source PaaS vs. SaaS Security Discovery Remote management Interoperability Supported standards
  57. 57. what makes a platform a good one?
  58. 58. whose chestnuts do we pull out of the fire?
  59. 59. Facebook Platform open API made it possible for third-party developers to create applications. src: http://www.digitaltrends.com/features/the-history-of-social-networking/ AppleStore Android GooglePlay Software advances (Hardware enablers) touch screens tablets / smartphones mobile computing Rather than offering a comprehensive social networking experience like the now-defunct Myspace and the struggling Google+, they instead specialize in a specific kind of interaction service that involves the sharing of public images (Instagram), the private sharing of images sharing (Snapchat), augmented reality (Foursquare), and location- based matchmaking (Tinder). People essentially use the various services in conjunction with other platforms to build a comprehensive, digital identity. what is the target? ease of use for its intended audience!!! ability to tinker and personalise it!!!! contextual background awareness…
  60. 60. three FBK CREATE-NET examples generic, target SMEs willing to digitilise their services, products, processes target SMEs and innovators in the African context modular gateway platform, target developers mostly
  61. 61. Integration API Raptorbox Problem addressed • Challenges for integration of IoT devices into existing product/service portfolio: • Complexity of integration of heterogeneous IoT devices into an existing infrastructure: • Interaction with IoT devices (device identification, protocol handling) • Security: secure communication, device and data access control • Scalability: • From few devices in trial phase to massive deployment of IoT connected devices • How to perform rapid prototyping to address fast business and tech validation cycles and fast delivery Service Bus Enterprise Systems Device integration and management made eas in a secure, scalable, configurable way courtesy of Fabio Antonelli
  62. 62. Our solution u Device Virtualization: u Common Device Modeling (“Web of Things” paradigm) u IoT Message Brokering: u Scalability by design u Multiprotocol support (http/https, MQTT, JMS, AMQP) u Data chaching for real-time event processing and querying u Configure your Business Logic for Rapid IoT Application Prototyping (Data and events workflow Editor) u Flexible Access Control & Authorization (ACLs) for devices and users u Secure Communication and Interaction with devices u Easy Integration via APIs exposing all available capabilties courtesy of Fabio Antonelli
  63. 63. Integration API Raptorbox Service Bus Enterprise Systems the Raptorbox IoT Data Broker COMMUNICATIONS SENSING GOODDATA VALUE GENERATION ROUTING FILTERING the more I understand the data, the better value I can provide… AGGREGATING INTERPRETING VALUE PROCESSINGlow high low high BADDATA JSON structured vs. stringified data
  64. 64. store significant data… Payload "properties": { "blood_glucose": { "allOf": [ { "$ref": "#/definitions/unit_value” }, { "properties": { "unit": { "enum": [ "mg/dL", "mmol/L” ] } } } ] } “literate” (relevant plugins / libraries) Raptorbox IoT Data Broker higher processing but… save storage space facilitate interpretation save network use “all blood glucose levels above a threshold”
  65. 65. Raptorbox target • system integrators mainly • focus on core service provisioning competences while exploiting interoperable platform for enriching those with interoperable IoT data harvesting • examples: SMEs digitalisation support, smart cities, e-health Integration API Raptorbox Service Bus Enterprise Systems why is technology transfer so hard?
  66. 66. WAZIUP Platform The EU-AFRICA WAZIUP platform (Actor view) App. Development App. Deploy Sensor registration App. Execution Developer Sensor owner App user Third party API integration Data provider courtesy of Corentin Dupont App source code data processing & analytics IoT PF IoT sensors
  67. 67. Architecture courtesy of Corentin Dupont
  68. 68. Behind the scenes courtesy of Corentin Dupont
  69. 69. courtesy of Corentin Dupont
  70. 70. A generic platform for many applications courtesy of Corentin Dupont
  71. 71. System overview CLOUD LORA GATEWAY SENSOR courtesy of Abdur Rahim
  72. 72. ElevUpIncubateur Connecté Benin Cattle rustling Senegal Fish farming Ghana Urban waste Togo Urbanatic Togo African IoT entrepreneurs courtesy of Abdur Rahim why is technology transfer so hard?
  73. 73. WAZIUP target • African community of developers • focus on core competences while exploiting ready-to-use open-source tools and components to cater for the needs of African businesses • examples: fish farming, precision agriculture, cattle rustling etc. App source code data processing & analytics IoT PF IoT sensors
  74. 74. From Research to Innovation in IoT: why is technology transfer so hard ? February 2018 IEEE WF-IOT Raffaele Giaffreda Chief IoT Scientist Twitter: @giaffred PART 2
  75. 75. EU AGILE PROJECT
  76. 76. AGILE – Open Source Modular Gateway for IoT The Challenges Decentralized IoT - GW Empowerment Control Devices Store and manage Data locally Create and run Apps Extensibility and Adaptability Adapt to different Verticals Modular extensible design Interoperability Protocols (for devices) Devices Cloud services GW HW platforms Developer communities Ease of Use Cloud-like DevOps Integrated management features Embedded devel. environment Facilitate code reuse Courtesy of Csaba Kiraly – AGILE Technical Coordinator
  77. 77. AGILE overview Dbus + REST APIs + SDK Low-level components connectivity, things, data, security, … Docker containerization Java, Node.js, Python, C++ components Docker compose based startup Yocto based OS lean OS, broad HW support App execution Embedded Dev UI, Cloud integration, Apps Open Modular HW simplify IoT GW design Pilot development 5 Pilots, 1 Testbed, 4 Artists, 2 Open Calls
  78. 78. AGILE HW Platforms Makers Gateway Industrial Gateway (Reference Design) Monitoring Station (Consolidated Design) Design for Modularity ATHENS Event Intrinsic modularity Modularity by expansion Faster delivery cyclesCourtesy of Paolo Azzoni – Eurotech
  79. 79. AGILE Makers’ gateway Courtesy of David Remon – Libelium
  80. 80. Industrial gateway (see D1.1-D.12 for details) Carrier module Courtesy of Paolo Azzoni – Eurotech
  81. 81. Rapid Prototyping overview Graphical App Development Maker’s Gateway Hardware Industrial Gateway Local Management Remote / Fleet Management Device Discovery Embedded Storage Visualization Software Stack Push to Cloud
  82. 82. a more comprehensive picture • IoT and Cloud (infrastructure) • Edge computing and Cognitive IoT (data) • Blockchains for Secure IoT • Promosing IoT (Industrial + eHealth) SENSORS PLATF’S EMBED’D SYSTEMS COMMS PROTOC’S DATA STRUCT’S IoT & Cloud Promising IoT Decentr. AI & IoT Existing and emerging trends in IoT Blockchains & IoT T-Shaped Model
  83. 83. IOT PLATFORM AS A SERVICE AKA IOT SERVICES SUPPORTED BY THE CLOUD IoT & Cloud Promising IoT Decentr. AI & IoT Blockchains & IoT
  84. 84. IoT, Edge Computing, Fog Computing challenges K. Skala, D. Davidovic, E. Afgan, I. Sovic, Z. Sojat: Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
  85. 85. Improve IoT through Cloud • constrained devices • limited processing power • limited battery power • limited networking • limited storage • limited support for scalable applications • advances in cloud computing (edge / fog computing, containers, micro- services) constrained to unconstrained offload, separating concerns… Cloud IoT
  86. 86. IoT and Cloud: derived trends Cloud IoT IoT in islands, localised applications, dawn of IoT experimentation Backend storage, security, processing, wider scope IoT services: common baseline supporting many apps Latency + privacy problem addressed Edge Computing for IoT ( ) Android “compliance” and integration
  87. 87. Why is edge / fog computing becoming more and more attractive • problems with latency • problems with systems reactivity • need for data privacy and ownership • technology progress – powerful cloud backend is a given • GPUs are enhancing the capabilities of affordable edge devices • problems with shipping increasing amounts of data from increasing amounts of devices • Internet of Media Things and Wearables personal bet? a future where you own your data and decide who gets to use it why tech transfer is so hard?
  88. 88. Edge for IoT: horizontal and vertical migration Dynamic instantiation of IoT functions (microservices) on edge cloud infrastructure GIoTS 2017: C. Dupont et al. “Edge computing in IoT context: horizontal and vertical Linux container migration”
  89. 89. More on IoT trends: distribution, decentralisation, resource sharing • IoT has increased the monitoring fabric • More and more IoT platforms claim to be providing the glue for addressing interoperability • With increasing numbers and pervasiveness, come the issues of control and capillary ownership • services become volatile • edge services for IoT bear a locality constraint • leading to three dimensional problem • 1. control of owned resources between Cloud, Edge, IoT • 2. variability over time • 3. blanket coverage impossible without additional cooperation Cloud Edge IoT Time Administrative domain
  90. 90. IoT & Cloud Promising IoT Decentr. AI & IoT Blockchains & IoT
  91. 91. IoT and Security https://www.pentestpartners.com/blog/new-wi-fi-kettle-same-old-security-issues-meh/
  92. 92. MIRAI DDOS ATTACK – October 2016
  93. 93. many levels of security • data encryption at transmission level • data encryption at storage level • policy-based access control • anonymise data • etc. • IoT and blockchains…(enable secure and logged exchange of IoT messages)
  94. 94. What is a Blockchain • Network of nodes offering a distributed database (ledger), that tracks transactions in “chains” of immutable blocks replicated among all participating nodes • Consensus mechanism: guarantees non-repudiable transactions • Rewarding mechanism: to incentivize mining activities and resources exchange (use of cryptocurrencies) Courtesy of Fabio Antonelli
  95. 95. How a blockchain works: an example
  96. 96. Blockchain Types History: • Bitcoin (Satoshi Nakamoto) • 2nd Generation: “programmable” blockchain (Smart Contracts creation) Types: • Public/consortium/private blockchains Different implementations: • Bitcoin, Ethereum, Hyperledger project (Linux Foundation)
  97. 97. Blockchain main characteristics • Decentralized: There is no single central database. Every transaction is recorded on every ‘block’ of a chain. Any block can be used to verify digital records. • Immutable: The decentralized nature of the database makes blockchain immutable. Publicly verifiable blocks with a permanent record of all transactions lend themselves well to automating auditing services. • Programmable: Blockchain can be programmed to execute transactions automatically, if certain pre-decided conditions have been met (Smart Contracts) Courtesy of Fabio Antonelli
  98. 98. Added Value for IoT • Trust and Reputation of IoT devices: • Non-Repudiable Device Identity • Security enforcement at the edge • Secure Traceability of Transactions and of Information: • in financial transactions, supply chains, and other processes involving involving IoT devices • transparency, auditability without the need to leverage on 3rd party trusted entities • Make consumer data more private • More Resiliency: • No single point of failure • IoT devices can autonomously interact with humans and other IoT devices: • including capabilities to perform automatic payments/value exchange tracking (digital currencies) courtesy of Fabio Antonelli
  99. 99. Use of blockchains in IoT related applications • more automated control of IoT devices “actions” • mart contracts for exchange of edge resources • new opportunities for localised IoT resources owners • more flexibility Cloud Edge IoT Time Administrative domain i.e. Ethereum lets you: Design and issue your own cryptocurrency Create a tradeable digital token that can be used as a currency, a representation of an asset, a virtual share, a proof of membership or anything at all.
  100. 100. Locality_X @Loc_X IoT Resources Pool Blockchains in IoT Edge Computing scenarios request commit probe reward / deny transact BC Client Smart Contract Record of (non-) fulfilment Blockchain for federated IoT resource pool generation X request
  101. 101. MAKING SENSE OF HARVESTED IOT DATA IoT & Cloud Promising IoT Decentr. AI & IoT Blockchains & IoT
  102. 102. The AI Revolution: The Road to Superintelligence http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
  103. 103. Machine learning and IoT • same type of problems ever since Marc Weiser seminal paper on Ubiquitous Computing was published (1993) • most successful technologies are those that disappear weaved into the fabric of our surrounding physical world • physical world needs to be represented digitally • modelling reality is still a complex problem • compared to 1993, we can certainly produce lots of more data with IoT capabilities and monitoring pervasiveness
  104. 104. unlocking a huge potential data data data data data data data data data data data data data data H/W motion presence location status patterns exist ... cause-effect in gathered data change over time SENSING constrained resources data goldmine and lots of siloed applications The Craft IoT Cognitive IoT motion presence location status observe cause-effect relationships train & gradually replace human in the loop derive patterns of ... interpret data adapt over time
  105. 105. how cognitive technologies and IoT can be leveraged upon to optimise network resource usage in a smart-city security monitoring application Alcatel Lucent Bell Labs / Thales courtesy of Marc Roelands 2011-14 EU Project
  106. 106. Extracting knowledge from data – domain expert modeling… • many bespoke machine-learning applications exist • however, still substantial overhead needed • loads of training data required… • smart-agriculture example • domain expert models need to assist machine learning experts to help them design algorithms that, based on collected data, can actuate according to model expectations • sometimes models need to be created through observation (lengthy process) • in both cases, a lot of validation data is needed to train and tweak algorithms • no wide applicability, no general purpose machine learning… • experience from iCore EU collaborative project
  107. 107. Problems we face • monitoring capability of edge devices has considerably increased • videos collected everywhere (also in “crowd-sourcing”) • TBytes of sensor data produced by a flight • cannot upload it all • need local processing, yet with limited capability devices (compared to powerful cloud computing racks) • interpret data with the objective of considering reducing its size without loss of information • compare a 5min video with picture of a cat at frame 259 of test-video.mp4 • camera is one type of sensor producing a stream of video frames • generalise to any sensor producing a stream of IoT data • noise, temperature, humidity…you name it • anomaly detection @ sensing sample #2234
  108. 108. An infrastructure perspective DUMB EDGE SMART EDGE FULL DATA RELEVANT METADATA FROM A… WE ARE MOVING TO A …
  109. 109. courtesy of Janjua Zaffar Feature extraction
  110. 110. Role of “augmented IoT” in Digital Twin representation Real World Situation Cam Mic Child a car, a hot pan etc. Digital Twin Situation DANGER ASSET “DISTANCE” ALARM THRESHOLD
  111. 111. AI at the edge – why? and why now? https://www.tractica.com/artificial-intelligence/artificial-intelligence-processing-moving-from-cloud-to-edge/ Federated Learning by Google Core ML at Apple Streaming (Facebook) vs. model update (Google)
  112. 112. GPU cluster BE Cloud AI Model 2 From monolithic to modular AI GPU cluster BE Cloud AI Model 1 GPU clusters BE Cloud AI Model ability to recognise a person, a car, a bus Cloud BackEnd (BE) IoT sensing / actuating fabric SENSE ACTUATE SENSE ACTUATE ability to recognise an unsupervised child, a hot stove, an electric plug Cloud BE Edge Cloud AI Model 1 “break-up” into separate modules AI Model 1 ? ?
  113. 113. Preparing the Infrastructure – decentralised AI Infrastructure flexibility research Deep learning, machine learning and AI mapped onto such an infrastructure Innovation in application domain Verticals Innovation infrastructure management enablers for flexible resources allocation machine learning and AI distributed models DUMB EDGE SMART EDGE FULL DATA RELEVANT METADATA
  114. 114. DEEP LEARNING Illustration by Justin Metz W ATCH THIS SPACE!!!! applicable in all scenarios exposing highly structured data the emergence of unsupervised learning… (+ advances in edge cloud computing)
  115. 115. Setting the scenes on IoT applications that are promising “It's no secret that the industrial IoT is where folks are hoping to make the big bucks.” Stacey Higginbotham IoT & Cloud Promising IoT Decentr. AI & IoT Blockchains & IoT
  116. 116. INDUSTRIAL IOT “It's no secret that the industrial IoT is where folks are hoping to make the big bucks.” Stacey Higginbotham
  117. 117. Image Credit: The Industrial Internet Consortium – April 2015 Infographic
  118. 118. TREND: we can sense and transmit more and more efficiently
  119. 119. why do we want to do that in an industrial context?
  120. 120. Software and hardware… • software industry • appliance / electronics • “SAP and Bosch team up on Internet of Things” • … The technology, for example, allows a production system to select the torque for each screwdriver's task, increasing efficiency ... • wow...what does it take to tighten a screw? • how much torque to apply when? what about replacing the screw driver? what about ensuring it is the right one for the type of screws? • sensing system and an actuator...plus contextual knowledge about type of screw, screw pitch and size, material (pre- sales)...data collection, interpretation (after-sales) enhance a particular task components, tools integration, know-how
  121. 121. enhance a particular task for what purpose??? Raccoltadati Descriptive what happened? Diagnostic why did it happen? Predictive what will happen? Preventive what should I do? Decision Actuation Decision support Decision automation human input requiredanalytics
  122. 122. TREND: servitization (sell & forget vs. sell and assist) enhance a particular task
  123. 123. Advantages of 4th industrial revolution • digitalisation of production process • digitalisation of product • monitoring during and after production • manufacturers and software house join forces • “just in time” production – with management of stock, stores, production value chain • products personalisation • reduced production and final product costs – competition • new business models tied to servitisation
  124. 124. all well, but… • need reliable technology • sensing and communications • security, dependability, servitization • (pre-sales / after-sales) • need performance • data-processing and edge cloud • need competences (choice, integration, deployment) • infrastructure • choice of technologies • interface between standards • middleware • flexible architectures • interface between standards • services and applications • what knowledge do we want to extract from data? • interface between standards • need technologists + domain experts, working side by side how to make it happen? enhance a particular task
  125. 125. need reliable technology • (sensing) – what to sense, size, durability, etc. • securely getting data out of sensors to the applications • what options for your production plant, assembly line, deployment environment… • 5G is a key enabler • reliable communications / protocols • energy efficiency • short round-trip delays • NB-IoT vs. Sigfox vs. LoRa components, tools
  126. 126. need performance appsense sense appprocess vs. edge cloud / fog computing components, tools
  127. 127. need competences • know-how! • infrastructure • choice of technologies • interface between standards • middleware • flexible architectures • interface between standards • services and applications • domain experts + technologists • solution design integration, know-how
  128. 128. eHEALTH and IOT
  129. 129. We live in an ageing society… The Economist: by 2050 the number of people aged over 80 will have doubled in OECD countries, and their share of the population will rise from 3.9% to 9.1% KPMG: number of care-home residents could grow by 68% over the next 15 years Problem: government subsidies reduced, ¼ of total care homes in the UK may close within 3 years (2016 article from The Economist) Solution: residential, home care increasingly attractive market FACT: home care on the rise
  130. 130. Wide spectrum of monitoring possibilities • Health parameters • Mobility (Indoor location) • Appliances usage • Environmental conditions • Progress towards goals Trend: consumer-grade devices becoming cheaper and more and more accurate and miniaturised, less invasive “What we call the “healthcare” industry is really a disease industry, dependent on an endless supply of distressed customers” M. Geddes More and more opportunities in the “wellness” and quantified self sector FACT: wide set of requirements
  131. 131. Tutorial Map SENSORS PLATF’S EMBED’D SYSTEMS COMMS PROTOC’S DATA STRUCT’S Emerging trends in IoT ResearchChallengesinIoT The business of IoT, business models, economic issues? IoT & Cloud Promising IoT Decentr. AI & IoT Blockchains & IoT
  132. 132. but… • many devices, as many apps and cloud backends…
  133. 133. furthermore…IoT standards…
  134. 134. health and wellbeing monitoring • quantified self in a smart home • plethora of devices • all use “device (gateway) cloud app” chains device-gateway protocols gateway-cloud IP cloud-app IP RESTful APIs MQTT pub/sub biggest “source of troubles” Operating Systems
  135. 135. one (not the only one) reason… https://qz.com/771727/chinas-factories-in-shenzhen-can-copy-products-at- breakneck-speed-and-its-time-for-the-rest-of-the-world-to-get-over-it/ FACT: IoT fragmentation
  136. 136. a bit of detail… • hardware products will be copied • hardware manufacturers need to minimise “copycats” risk factor • high sell vs cost markup (make profit while you can) • bundle software services (i.e. smart ways of processing / visualising collected data) • software lock-in realised with additional cloud services (i.e. a “cool App” that everyone wants to use)
  137. 137. and so what? • many apps to install • devices more expensive than they need to be • apps not interoperable • but the worst is we give away the right to control who uses our personal data and for what reason… FACT: dreadful user experience FACT: we lost control of our data
  138. 138. IoT devices and gateways – the vendor strategy • Cannot create a business based only on hardware • Software lock-in realised with additional cloud services (i.e. a “cool App” that everyone wants to use) • Reinforce the message: “all your personal data are in the hands of the companies whose hardware you use to collect it!” • Moral need to intervene and do something about it… FACT: we lost control of our data
  139. 139. a quick recap… • contextual IoT technology background • highlighted two main problems 1. interoperability hurdle 2. control over my own data what can we do about it?
  140. 140. Walking the “research – innovation – business” path • EU FP7 COMPOSE 2011-14 • EU H2020 IA UNCAP 2015-17 • EIT Digital ESSENCE 2017 • EU H2020 AGILE 2016-18 research on IoT interoperability services Innovation Action with integration of an IoT Broker into an eHealth project business solution leveraging on developed assets ASSETS Interoperable Gateway
  141. 141. Infrastructure assets Rapid IoT Application Prototyping (Data and events workflow Editor) Easy Integration via APIs exposing all available capabilities I can chose for a subset of my data never to leave my home gatewayInteroperable Gateway Interoperate your own IoT devices Data Mgmt APIs Modular IoT gateway Scalability by design Multiprotocol support (http/https, MQTT, JMS, AMQP) Data caching for real-time event processing and querying Rapid IoT Application Prototyping (Data and events workflow Editor) Easy Integration via APIs exposing all available capabilities Flexible Access Control & Authorization (ACLs) for devices and users my data in the cloud BUT…I am in control
  142. 142. Secure, Permanent Storage IoT Data Broker (cloud) data sources data sources data sources data sources data sources data sources IoT Data Broker (gateway) IoT data (direct) IoT data (via gateway) APPLICATIONS CEP, data processing access control PROCESSING SENSING MQTT, STOMP, CoAP, REST, WebSockets eHealth solution – building blocks 1 2 3 2a
  143. 143. Secure, Permanent Storage IoT Data Broker (cloud) data sources data sources data sources data sources data sources data sources IoT Data Broker (gateway) IoT data (direct) IoT data (via gateway) APPLICATIONS CEP, data processing access control PROCESSING SENSING MQTT, STOMP, CoAP, REST, WebSockets eHealth solution – our assets 1 2 3 2a
  144. 144. Specialising the architecture Dignity Autonomy Independence MONITOR GAIN Better level of life Biosensors Indoor/outdoor localization Home automation
  145. 145. Router Exploiting IoT in the Health & Wellbeing domain WEB ESSENCE GuardiApp DOCTOR GUARDIAN ESSENCE Friends&Family PATIENT HOME CLOUD APPS APPS
  146. 146. ESSENCE in one (busy) slide J Android App Router Fibaro GW Hue GW oxi sca prs lamp motion light smoke temp panic button CHINO CEP Notification WebApp ESSENCE GuardiApp PATIENT DOCTOR GUARDIAN ESSENCE Friends&Family Comm Module Auth&Login glu HOME
  147. 147. ESSENCE in one (busy) slide J Android App Router Fibaro GW Hue GW oxi sca prs lamp motion light smoke temp panic button CHINO CEP Notification WebApp ESSENCE GuardiApp PATIENT DOCTOR GUARDIAN ESSENCE Friends&Family Comm Module Auth&Login glu HOME
  148. 148. “Gateway – Cloud” IoT Platform FBK MAIN FOCUS interoperability hurdle control over my own data
  149. 149. Value-add infrastructure – a business context
  150. 150. The collaboration with Nively startup • Help an existing product to extend their solution • huge enhancement potential with IoT • visual alerts • notifications • aided support • smart home interactions • but “off the shelf” products not easy to integrate
  151. 151. innovation catalyst…the ESSENCE project Diversity of requirements Diversity of siloed IoT solutions + => + =>users technology startup FACT: wide set of requirements FACT: IoT fragmentation FACT: dreadful user experience FACT: we lost control of our data FACT: home care on the rise
  152. 152. The role of our research center technology enhancement market reach integrate more IoT devices differentiate from competition value-add services enlargemarket segment value-add creation
  153. 153. 158 The team
  154. 154. Pilots • Municipality of Nice (France) • APSP Vannetti (Italy) la Direction de la Santé de la Ville de NiceApartment Apartment Apartment Apartment Reception Doctor Family
  155. 155. Next Steps… • Huge market potentials in the eHealth domain drafting a commercial collaboration framework…
  156. 156. Another example…Smart Agriculture
  157. 157. Courtesy of Paolo Spada, Luca Capra
  158. 158. the problem Courtesy of Paolo Spada, Luca Capra
  159. 159. the solution Courtesy of Paolo Spada, Luca Capra
  160. 160. in a nutshell Courtesy of Paolo Spada, Luca Capra
  161. 161. business aspects Courtesy of Paolo Spada, Luca Capra
  162. 162. THE BUSINESS OF IOT
  163. 163. WHY ISN’T IT HAPPENING YET?
  164. 164. where is the IoT? • no broad set of applications encompassing “one IoT” • with mobile phones and personal computers it was easier • IoT devices very diverse, yet we tend to blur boundaries • losing ability to tackle separately different markets DISCLAIMER: no business expert but have matured insights into the business of IoT that might be useful to share
  165. 165. All IoT examples but… smart locks thermostats lights health “Home” power OK costs LOW “industrial” power LOW costs No constraintsWIDE SPECTRUM OF REQUIREMENTS
  166. 166. SOME KEY QUESTIONS •what business model? •is this worth x Eur/month… •to me? •to my intended market audience? •to my public administration?
  167. 167. Return on investment (ROI) • EXAMPLE 1 • I spend a $ to buy a bottle of water because I am thirsty • the (immediate) need = I am thirsty • who benefits? = me (private) • willingness to pay for it = I need it badly • when do I benefit = as soon as I get my bottle • I make an (private) investment, the benefit is immediate • VERY SHORT CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE B2C • EXAMPLE 1.b • I spend $ to buy an iPhone • the (immediate) need = I need a cool device • who benefits? = me (private) • willingness to pay for it = can do cool things with it • when do I benefit = as soon as I get it • I make an (private) investment, the benefit is immediate • VERY SHORT CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE location is key – booth next to a fountain? “coolness” is key – no “cheap look” please… IDENTIFY YOUR POTENTIAL MARKET TARGET…
  168. 168. Return on investment (ROI) • EXAMPLE 2 • I spend money to make my house energy efficient • the (not so immediate) need = I need to save money on my energy bills • the (good for a common cause) need = I need to make my life more sustainable • who benefits? = me (private), the environment • willingness to pay for it = I need it (not so badly), the environment needs it (not so badly) • TIME DIMENSION • when do I benefit = after I paid the bills for needed equipment with the money I saved • I make an investment, the benefit might be for someone else or not materialise until later • LONG-ISH CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE BUT… B2G2CB2C • EXAMPLE 2.b • smart-lighting • the (not so immediate) need = I need to save money on my energy bills • the (good for a common cause) need = I need to make my city more sustainable • who benefits? = the environment • willingness to pay for it = the city balance sheet needs it (in a couple of years, not so badly), the environment needs it (not so badly) • TIME DIMENSION • when do I benefit = after I paid the bills for needed equipment with the money I saved • I make an investment, the benefit might be for someone else or materialise when it is too late • LONG-ISH CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE BUT…
  169. 169. Return on investment (ROI) • EXAMPLE 3 • I have a business and I want to digitilise it • spend money to make my production process more modern and efficient… • the (not so immediate) need = I need to gain competitive advantage • the (good for a common cause) need = I need to gain insights into my business operations • who benefits? = my biz (private) • willingness to pay for it = I need it (not so badly), long-term gains • TIME DIMENSION • when do I benefit = as soon as I am in a position to transform gathered data into differential advantage that drives more customers to buy what I sell or reduces operating costs etc. • I make an investment, the benefit is not immediate and depends on a proper strategy • LONG CYCLE, UNTANGIBLE B2B2C
  170. 170. The value (and diversity) of data • the importance of bespoke modeling – multi-disciplinarity and adjacent domain experts interactions • cycles of learning (modeling) before I can be predictive and even longer before I can be prescriptive… • sensing and influence on results... • IS IT WORTH IT? (SENSE – DECIDE – ACTUATE) Example: motors manufacturing biz vibration, current, torque MTBF: 60000 hours (!) Raccoltadati Descriptive what happened? Diagnostic why did it happen? Predictive what will happen? Preventive what should I do? Decision Actuation Decision support Decision automation human input requiredanalytics the ROI CYCLE
  171. 171. market segmentation ROI COSTS IMPACT B2C B2B B2B2G SHORT LONG LOW LOW HIGH HIGH Descriptive what happened? Diagnostic why did it happen? Predictive what will happen? Preventive how to avoid it? build hindsight what insight do I need? foresight and optimise Time complexity potential gains
  172. 172. California US trip 2016 – know who are your best customers
  173. 173. WHO we solve the problems for and WHY • WHO • application developers (rapid prototyping) • system integrators • system admin of eHealth • API framework managers u WHY u rapid development saves costs & time u agility u easy integration u hide complexity, Web-based APIs 9
  174. 174. key message – who is your target? • Cisco (Jasper), IBM (Bluemix), GE (Predix) … • IoTango, Trilogis etc. • propose a reference framework for validation of how to break-down a complex problem space into more “palatable” “mouth-sized” chunks
  175. 175. up-front investments and ROIs IS IT WORTH IT?
  176. 176. OPPORTUNITIES ARE TREMENDOUS WARNING!!! THIS IS DAUNTING IF YOU WANT TO EAT IT ALL ROI CYCLE LENSES MIGHT HELP NEED TO BREAK IT DOWN IN SMALLER CHUNKS BUSINESS MINDSET
  177. 177. Conclusions and Future Directions • IoT technology challenges are giving way to integration challenges and most importantly to business challenges • Interoperability becoming less and less of a stumbling block, focus on IoT platforms that address also those issues • yet, platform assets without a focus on application domain lead nowhere • T-shaped models currently best bet for building success business stories • Decentralisation technologies • Increasing distribution and wide-coverage footprint • Blurring of boundaries between Cloud and IoT • Blockchains-based solutions • Artificial Intelligence embedded in IoT
  178. 178. Thank you!

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