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Fog Computing is the Future of the Industrial Internet of Things

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Originally aired 8/24/2016

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Fog Computing is the Future of the Industrial Internet of Things

  1. 1. Fog  Compu)ng  is  the  Future  of  the  IIoT   Stan  Schneider,  PhD.    RTI  CEO,  IIC  Steering  Commi?ee  
  2. 2. What  is  Fog?   •  Weather:  Fog  =  Cloud  close  to  the  ground   •  IoT:  Fog  =  Cloud  close  to  the  things       –  Flexible,  powerful  computa)on  outside  a  data   center   •  Neither  is  a  crisp  term  
  3. 3. My  (Easy)  Defini)ons   •  Cloud   –  An  elas)c  compu)ng  environment  in  a  $b  data  center   •  IT  (Informa)on  Technology)   –  SoQware  (and  hardware)  that  runs  in  the  cloud   –  Also,  sys  admins  and  business  programmers  who  run  the  above   •  Things   –  Any  physical  device  or  system  that  has  compu)ng.       –  Soon,  everything  manmade.   •  OT   –  Compu)ng  that  actually  controls  and  powers  “things”   –  Also,  engineers  who  implement  the  things   •  Edge   –  IT’s  collapsed  view  of  the  real  world  not  in  the  cloud   •  Fog   –  Compu)ng  that  makes  OT  things  intelligent  with  distributed  or   elas)c  compu)ng  not  in  a  data  center.  Usually  layered.   •  IoT   –  All  of  the  above   •  IIoT   –  IoT  that  consumers  don’t  buy  (except  AD  cars)  
  4. 4. The  smart  machine  era  will  be  the  most  disrup4ve  in  the  history  of  IT   -­‐-­‐  Gartner  
  5. 5. The  IIoT  Disrup)on   The real value is a common architecture that connects sensor to cloud, interoperates between vendors, and spans industries Common  technology  that  spans   industries  brings  bold  new  approaches   and  enables  fast  change  
  6. 6. 250+  companies  strong   Goal:  build  and  prove  a  common   architecture  that  spans  sensor  to   cloud,  interoperates  between  vendors,   and  works  across  industries  
  7. 7. 250+  Companies,  25+  Countries Contribu9ng  Members   IIC  Founding  Members   The  IIC  Created  the  Industrial  Internet  of  Things  Market  
  8. 8. RTI’s  Role  in  the  IIC   Connec)vity   Safety  Team   Architecture   Team   Distr  Data  Mgmt   &  Interoperabilty   IIC  Steering  Commi?ee   Legal   Working  Group   Marke9ng   Working  Group   Business   Strategy   Working  Group   Security   Working  Group   Technology   Working  Group   Testbeds   Working  Group   Liaisons  Team  Ecosystem   Testbed  SubC   IIC  Staff  
  9. 9. ©2016  Real-­‐Time  Innova)ons,  Inc.  
  10. 10. The  “Fog  Node”   ©2016  Real-­‐Time  Innova)ons,  Inc.   Remote  management   Security   Elas)c  compute   Analy)cs  /  cogni)on   Data  management   Data  management   Applica)on  services   Connec)vity  
  11. 11. RTI’s  Experience   •  ~1000  Projects   –  Healthcare   –  Transporta)on   –  Communica)ons   –  Energy   –  Industrial   –  Defense   •  15+  Standards  &  Consor)a  Efforts   –  Interoperability   –  Mul)-­‐vendor  ecosystems  
  12. 12. RTI  IIoT  Influence   Top  10  Companies   Industrial  IoT   IBM   AT&T   Cisco   GE   RTI   Consumer  IoT   Amazon   Google   Samsung   Apple   MicrosoQ  
  13. 13. Why  is  Fog  Compu)ng?  
  14. 14. The  Internet  Didn’t  Change  Most  Industries   1991   2015  
  15. 15. ©2016  Real-­‐Time  Innova)ons,  Inc.   IT  integra)on   OT  device   network   Pervasive  data   availability   Elas)c   compu)ng   Core   connec)vity   standard  (DDS)   Industry  Terminology  
  16. 16. DataBus Disrup)on:  Distributed  Architecture   Private  cloud  (fog)   Public  cloud   Raw   Image   Secure  
  17. 17. What  Must  Fog  Do?   What  do  IIoT  systems  need  from  the  infrastructure?  
  18. 18. Ensure  Reliable  Availability   •  What:  Con)nuous  opera)on  >>  99.999%   •  How:  Easy  redundancy,  no  servers  
  19. 19. Guarantee  Real-­‐Time  Response   •  What:  response  <  100us,  even  with  load,  complex  data  types,  many  flows   •  How:  peer-­‐to-­‐peer,  mul)cast,  data  path  op)miza)on  
  20. 20. Manage  Complex  Data  Flow  and  State   •  What:  Find  and  deliver  the  right  informa)on  to  the  right   place  at  the  right  )me   •  How:  Data  centric  selec)ve  source  filtering  
  21. 21. Ease  System  Integra)on   •  What:  Manage  interfaces  between  teams  and  modules   •  How:  Explicit  interface  design,  evolu)on,  and  enforcement  
  22. 22. Control  Dataflow   •  What:  Control  dataflow  between   applica)ons  to  match  situa)on   •  How:  Transport  independence,  Quality  of   Service  (QoS)  control,  dataflow  security   ©2016  Real-­‐Time  Innova)ons,  Inc.  
  23. 23. How  Does  This  Work?   Technology  
  24. 24. Systems  are  About  the  Data   Data  Centricity  Defini)on     a)  The  interface  is  the  data.       b)  The  infrastructure  understands  that  data.       c)  The  system  manages  the  data  and  imposes   rules  on  how  applica)ons  exchange  data.       Database   Databus   Data  centric  storage  and   search  of  old  data   Data  centric  sharing  and   filtering  of  future  data   Applica)on   Applica)on   Message  centric   Remote  Objects   SOAs   Applica)on   Applica)on   Data  
  25. 25. Data  Centric  is  the  Opposite  of  OO   Object  Oriented   •  Encapsulate  data   •  Expose  methods   Data  Centric   •  Encapsulate  methods   •  Expose  data   Explicit   Shared   Data   Model  
  26. 26. The  DDS  Standard   Real-­‐Time   Analy)cs  &   Control   Operator  HMI   Sensors   Actuators   Comms  /  Cloud   Integra)on   The  Data  Distribu)on  Service  (DDS)  is  the  Proven  Data   Connec)vity  Standard  for  the  IoT   Any  language,  OS   Extensive  QoS   Security   Peer-­‐to-­‐peer   Reliable   Mul)cast  
  27. 27. DDS  is  Different!   Data-­‐Centric   DDS   Shared  Data  Model   DataBus   Point-­‐to-­‐Point   TCP   Sockets   Client/Server   MQTT   XMPP   OPC   CORBA   Brokered   ESB   Daemon   Publish/Subscribe   Fieldbus   CANbus   ZeroMQ   JMS   Queuing   AMQP   Ac)ve  MQ  
  28. 28. Data  Centric  SoQware  Integra)on   •  Global  Data  Space   –  Automa)c  discovery   –  Read  &  write  data  in   any  OS,  language,   transport   –  Type  Aware   –  Direct  peer-­‐to-­‐peer   –  Redundant  sources/ sinks/nets   •  No  Servers!   •  QoS  control   –  Timing,  Reliability,   Liveliness,  Redundancy,   Ordering,  Filtering,   Security   Shared  Global  Data  Space     DDS  DataBus   Actua)on   Percep)on   Planning  &  Nav   Cloud   Offer:  Write   obstacle  update   100x/sec   Reliable  for  10  secs   Request:  Read  obstacle  10x/sec   If  distance  <  200  m  &&  velocity  >  10   Obstacle   Topic   Type   QoS   Obstacle   Topic   Type’   QoS’   Vehicle   Topic   Type   QoS   Intersec)on   Topic   Type   QoS   Obstacle   Topic   Type   QoS  
  29. 29. Secure  Dataflow   •  Dataflow-­‐Level  Security   –  Control  r,w  access  to  each  data  item  for   each  func)on   –  Ensures  proper  dataflow  opera)on   •  Complete  Protec)on   –  Discovery  authen)ca)on   –  Data-­‐centric  access  control   –  Cryptography   –  Tagging  &  logging   –  Non-­‐repudia)on   –  Secure  mul)cast   •  No  code  changes!   •  Plugin  architecture  for  advanced  uses   CBM  Analysis  PMU   Control   Operator   State   Alarms   SetPoint   Topic  Security  model:   •  PMU:  State(w)   •  CBM:  State(r);  Alarms(w)   •  Control:  State(r),  SetPoint(w)   •  Operator:  *(r),  Setpoint(w)  
  30. 30. Unit DataBus Unit DataBus Take  it  to  Massive  Scale   •  Each  level  of  the  hierarchy   has   –  Data  model   –  Discovery   –  Security  domain   •  System-­‐of-­‐systems  require   –  Subsystem  export  control   –  Data  model  transla)on   –  Discovery  control   Intelligent   Machines   Intelligent   Systems   Intelligent   Industrial   Internet   Cloud DataBus Site DataBus Intelligent   System  of   Systems   Unit DataBus Sense   Act   Think   HMI   Machine DataBus Think   HMI   Machine DataBus Sense   Act   Think   HMI   Machine DataBus
  31. 31. Unit DataBus System  of  Systems   •  Each  subsystem  at  each  level   has  a  different  global  data   space   •  To  build  a  Big  System,  map   data  spaces   Sense   Act   Think   HMI   Machine DataBus Ven)lator  Internal:   •  Gas  flow  rates,  backpressures,   feedback,  real-­‐)me  compute     •  Many  readers,  writers,  dataflow,   compute,  specs,  security  controls   External:   •  Only  “exported”  data   •  Physiological  parameters,   pa)ent  status   •  Different  schema,  QoS   •  One  x.509  ID   •  Each  device  looks  like  a  small   system   •  Intelligent  compute   Key  “fog  bridge   node”  component.    
  32. 32. What  Must  a  “Fog  Bridge  Node”  Do?   •  Bridge   –  Data  models   –  Protocols   –  Security  domains   –  Communica)on  pa?erns  (!)   •  Isolate  subsystems   –  Control  export   –  Filter  access   –  Translate  models   –  Ease  fast  discovery   •  Note   –  Services  can  be  fully   redundant   –  Conceptual  design!    Node   implementa)on  may  include   many  services,  compute,  etc.   Other   protocols                        Rou)ng  Service      Pluggable  Adapters   Transforma)on  Engine   System   Super  System   Subsystem   Subsystem   Subsystem   Subsystem  
  33. 33. A  Detailed  Example:  Smart,   Connected  Hospitals  
  34. 34. Problem:  Mistakes  Kill   Hospital  error  is  the  3rd    leading  cause  of  death  in  the  US  
  35. 35. Pa)ent-­‐Controlled  Analgesia   PCA  is  widely  used,  and  considered   safe…   …but  2-­‐3  pa)ents  die  every  day  in  the   US  from  opiate  overdose  from  PCA   Figure  2.    Pa)ent  Controlled   Analgesia  (PCA)   The  pa)ent  presses  a  bu?on  to   receive  intravenous  pain  medica)on.     Monitoring  is  not  typically  used  due   to  high  false/nuisance  alarm  rate.      
  36. 36. What  Can  Change  This?   ECRI Institute identifies alarm hazards as its Top Health Technology Hazard for 2013   Clinicians exposed each day to tens of thousands of alarms   Nineteen out of 20 hospitals surveyed rank alarm fatigue as a top patient safety concern   Hospital Errors are the Third Leading Cause of Death in U.S., and New Hospital Safety Scores Show Improvements Are Too Slow New research estimates up to 440,000 Americans are dying annually from preventable hospital errors.  
  37. 37. Solu)on:  Smart  Pa)ent  Monitoring   Data  Bus   Supervisory   Services   Pa)ent  Mgmt   Device  Mgmt   Systems  Health   Interac)on   Checking   Logging    CDS     Algorithm     #1   Lab     Data   SPO2   CO2   Pa)ent   Hx   Infusion  Pump   Se{ngs   IV  Pump   Control   Infusion  Pump   Measured   Values  
  38. 38. CDS  Data  Architecture   Room  Domain   Central  Domain   Admin  Domain  (Cloud)   Pa)ent  Monitoring    Devices   Worksta)ons,   Storage,  Historian   Gateway,  IX,  Enterprise,  3rd  Party   Rou)ng  Service  “fog   bridge  node”   Rou)ng  Service  “fog   bridge  node”  
  39. 39. CDS  System  of  Systems  
  40. 40. GE  Healthcare's  IIoT  Architecture   "GE Healthcare is leveraging the GE Digital Predix architecture to connect medical devices, cloud-based analytics, and mobile and wearable instruments. The future communication fabric of its monitoring technology is based on RTI's data-centric Connext DDS platform.” -­‐-­‐  Ma?  Grubis,  Chief  Engineer,  GE   Healthcare's  Life  Care  Solu)ons   h?p://www.r).com/mk/webinars.html#GEHEALTHCARE   Every  fog  bridge  node  routes   between  data  models,  QoS,   security  domains  
  41. 41. ©2016  Real-­‐Time  Innova)ons,  Inc.  
  42. 42. ©2016  Real-­‐Time  Innova)ons,  Inc.   Room  or  ward  domain   Rou)ng  Service  “fog   bridge  node”   Higher-­‐level  Rou)ng   Service  “fog  bridge  node”  
  43. 43. A  Detailed  Example:  Smart,   Distributed  Power  
  44. 44. Problem:  Central  Genera)on   The  Future  Power   Grid  is  Distributed  
  45. 45. Goal:  Generate   more  than  load   Today’s  Grid  Wastes  Solar  Power   Power  Plants   Central  Sta)on   Sub  Sta)ons   Distributed  Load   15-­‐min  state  es)ma)on   “Spinning  reserves”   Balance  grid  with  frequency  control   Add  solar:   add   uncertainty   Need  more   reserves!  
  46. 46. Varia)on  Forces  Overgenera)on   Spinning   Reserves   Demand   with  no  solar   Demand   with  solar  
  47. 47. Solu)on:  Field  Message  Bus   Power  Plants   Central  Sta)on   Sub  Sta)ons   Distributed  Load   State  es)mates  more  stable  Much  lower     “spinning  reserves”   Local  loops  connect  genera)on,     load,  &  ba?ery  storage  Goal:  Generate   more  than  load  
  48. 48. Local  Loops  &  Storage  Reduce  Spinning  Reserves  
  49. 49. Grid  Moderniza)on   •  The  OpenFMB  (Field  Message  Bus)  architecture   integrates  solar,  wind,  and  storage  into  the  grid   •  Dozens  of  vendors,  several  u)li)es,  and  standards   organiza)ons  are  building  devices,  user  interfaces,   and  analy)cs   •  DDS  powers  OpenFMB  communica)ons  
  50. 50. Field  Message  Bus  Concept   Rou)ng  Service  “fog   bridge  node”  
  51. 51. IIC  Microgrid  Testbed  Architecture Goals   •  Efficiently  use  solar,  wind,  &  EVs   •  Create  an  open  marketplace   •  Prove  viability  of  the  databus  pa?ern   Leads   •  RTI:  DDS  middleware  and  system  integra)on   •  NI:  Engineering  soQware  and  hardware   •  Cisco:  Grid  communica)ons   Phases   1.  Proof  of  Concept  at  Na)onal  Instruments   2.  Realis)c  simula)on  at  Southern  Cal  Edison   3.  Live  test  at  CPS  Energy  San  Antonio  Grid  of  the   Future  
  52. 52. Modular  Applica)on  Development   A  product  group  within   ABB’s  Grid  Automa)on   business  unit  chose  RTI   Connext  DDS  to:   –  Enable  modular   substa)on  automa)on   applica)ons   –  Significantly  reduce  the   cost  of  development   –  Leverage  a  standards-­‐ based  commercial   product  
  53. 53. Distributed  Intelligent  Control   •  Wind  turbine  farms  can  include  500   turbines   •  Gust  control  across  the  array   requires  fast  communica)ons  with   dynamic,  selec)ve  filtering   •  DDS  enables  large,  distributed   intelligent  machines  
  54. 54. Power  Cri)cal  Infrastructure   •  DDS  controls  the  6.8  GW  GC  Dam   –  Largest  power  plant  in  North  America   –  Fastest-­‐responding  major  power  source  on   the  Western  Grid   •  RTI  system  live  since  Jan  2014   •  USACE  deploying  across  the  80-­‐dam  US   system   •  Connext  DDS  met  the  challenges   –  Extreme  24x7  availability   –  Wide  area  communica)ons   –  Mul)-­‐level  rou)ng   –  High  security   –  300k  data  values  
  55. 55. Ultra  Available  Plant  Control   Radar   Radar   Displays   Logging   Alarming   Monitor   Interested  in   many  quan))es   IPC   IPC   IPC   Segment  Bus   Redundant  Rou)ng   fog  bridge  node   IPC   IPC   IPC   Segment  Bus   IPC   IPC   IPC   Segment  Bus   VPN/Firewall   Local  data  space   VPN/Firewall  VPN/Firewall   Control  Room  Bus   Control     Room   Migra)on   Server  
  56. 56. A  Detailed  Example:   Autonomous  Vehicles  
  57. 57. Problem:  Ge{ng   There  is  Dangerous   and  Slow  
  58. 58. RTI’s  Deep  Exper)se  in  Autonomy   •  Connext  DDS  enables  autonomy   –  Ensure  reliable  data  availability   –  Guarantee  real-­‐)me  response   –  Manage  complex  data  flow  and   state   –  Ease  system  integra)on   –  Allow  any  network   –  Build  in  security  from  the  start   –  Make  deployment  flexible   –  Ease  safety  cer)fica)on   –  Adapt  Intelligence   –  Connect  Vehicle/Cloud  Systems   Founded  from   Stanford  Aerospace   Robo)cs  Lab  
  59. 59. A  New  Freedom:  Cars  -­‐>  Robot  on  Wheels   •  Faster,  safer,  cheaper,  farther,  easier   •  30%  of  all  US  jobs  will  end  or  change   –  Trucking,  delivery,  traffic  control,  urban   transport,  child  &  elder  care,  roadside   hotels,  restaurants,  insurance,  auto   body,  law,  real  estate,  leisure   •  The  driverless  car  is  the  biggest   disrup)on  since  the  horseless   carriage    
  60. 60. ©2016  Real-­‐Time  Innova)ons,  Inc.   Status  Feb  2016  
  61. 61. ©2016  Real-­‐Time  Innova)ons,  Inc.   Status  Feb  2016  
  62. 62. Autonomous  Driving   Status  Feb  2016  
  63. 63. Cloud  Services   Integra)ng  Intelligent  Components   Sensing   Planning   Radar,  LIDAR   Vehicle  Pla‚orm   Naviga)on   Error     Management   Visualiza)on   Situa)on  Analysis   Situa)on  Awareness   Vision   Fusion   Cameras,  LIDAR,   Radar     …   Data  Fusion   Logging  Vehicle  Control   Localiza)on   DDS  Bus   Traffic   Maps   DDS  Bus  
  64. 64. ©2016  Real-­‐Time  Innova)ons,  Inc.  
  65. 65. Vehicle/Cloud/Infrastructure  Systems   Physio-­‐Control  supplies   emergency  response  medical   equipment    to  60%  of  the   world’s  emergency  vehicles     "Physio-­‐Control  is  u4lizing   RTI  Connext  DDS  to  exchange   cri4cal  pa4ent  care   informa4on  throughout  the   system  of  care.“     -­‐-­‐  Dale  Pearson,  VP  Data  Solu)ons   We  envision  a  society  in  which   no  person  dies  from  acute,   treatable  medical  events  
  66. 66. Is  Fog  for  You?  
  67. 67. What  Can  You  Do  with  Fog?   •  Integrate  intelligence   •  Scale   •  Improve  reliability   •  Enforce  security   •  Enable  new  business  models   •  Respond  to  the  real  world   •  Integrate  many  vendors   •  Prevent  human  error   •  Save  development  costs   •  Enable  system  evolu)on   •  Connect  IT  to  OT   •  Improve  safety   •  Op)mize  resource  use   •  Enable  open  architecture   •  Ease  system  integra)on   •  Reduce  development  risk    Join  the  21st  century  
  68. 68. Is  Data-­‐Centric  Fog  for  You?   •  Reliability:  Severe  consequences  if  offline  for  5ms  (or  5  min)?   •  Real-­‐)me:  measure  in  ms  or  µs?     •  Interface  scale:  10+  applica)ons/teams?       •  Data  scale:  1k+  addressable  data  items?   •  Architecture:  Next  genera)on  IIoT?   3+  Checks?  
  69. 69. The  IIoT  Disrup)on   The real value is a common architecture that connects sensor to cloud, interoperates between vendors, and spans industries Common  technology  that  spans   industries  brings  bold  new  approaches   and  enables  fast  change  

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