Can IoT Disrupt the Product-based
Business Model in Light of 5G?
Dr Shoumen Palit Austin Datta
Research Affiliate, School of Engineering, MIT ● Massachusetts Institute of Technology
Senior Vice President, Industrial Internet Consortium ● www.iiconsortium.org
Eliminate “roadmap” and embrace “compass”
The Principles and Practice of Connectivity
The Elusive Quest for Monetization
Executive Summary
COM
ANALYTICS
EDGE
SERVICE
COGNITION
TIME
IoT
CPS
It is likely to be an evolution … potential for revolution is slim.
5G
What is absent from this illustration?
HEALTHCARE
5G
Re-visiting POTS?
Issues and Politics on the Road Ahead
D
S
R
C
WiFi
4G
L
T
E
https://docbox.etsi.org/workshop/2014/201402_ITSWORKSHOP/S04_RADIO/TOYOTA_SPAANDERMANforKENNEY.pdf
CR
Frequency GHz
www.slideshare.net/zahidtg/ltebased-unlicensed-carrier-offloading-huawei
http://s3.amazonaws.com/sdieee/205-LTE+Direct+IEEE+VTC+San+Diego.pdf
http://bit.ly/LTE-D
http://bit.ly/LTE-V
Social integration of autonomous
vehicles with public road traffic?
2033-2035
The Wealth of Nations ● Nature of the Firm (Transaction Cost Economics)
Economic history and data related to Textile, Railway, Automobiles and Computers taken from work by Norman Poire
Adoption
1853
Textile
1800
1913
Railway
1853
1969
Auto
1913
2020
Computer
1969
2061
Agents
ML
2005
2081
Nanotech
DLV, CPS
3DP / IoT
2020
2100
IoT / IIoT
Cyberwar
Hydrogen
Autonomous
Cars, Fusion
2040
2035
1771
1825
Technology Surfaces
Industrial Revolution
Atoms
1886
1939
1977
1959
AI
1991
2010
1995
2007
Public Autonomous
Internet Vehicles
Systems IoS
Knowledge Driven Economies of Scale
Bits
It takes about 28-30 years for an idea to be socialized before it is accepted and adopted.
1999 was the birth year for IoT concept. Expect exponential growth of IoS ~ 2025-2026.
Simple Problem
EXAMPLE
plastic bag
How does an autonomous vehicle
understand the difference between
an object without threat in a run
time collision avoidance context?
Without algorithmic solutions,
even a harmless plastic bag in
the air may cause an accident.
Oh I see a plastic bag
What is the “brain” of the autonomous vehicle thinking?
Where is the computational brain? In the cloud? In the fog? In the mist?
Critical tools for real-time image identification and semantic context of object
Neural Image Caption (NIC) Generator
Translates images to natural language
http://arxiv.org/pdf/1411.4555v1.pdf
To translate languages, Recurrent Neural Network (RNN) transforms a French sentence into a vector representation, and a
second RNN uses that vector representation to generate a target sentence in German. Replace first RNN and input words
with deep Convolutional Neural Network (CNN) trained to classify objects in images and add known classes of objects in
semantic baffles with corresponding behavior (plastic bag versus wooden plank) with assigned probability of object in the
image (environment). Feed CNN’s rich encoding of the image into a RNN designed to produce phrases. We can then train the
whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match
the training descriptions for each image. The natural language spoken by human (inside vehicle) better trains the algorithms.
Author’s idea is adapted from → http://googleresearch.blogspot.co.uk/2014/11/a-picture-is-worth-thousand-coherent.html
Mist Computing
“Edge Intelligence”
“Mist Computing” doesn’t exist. It’s a suggestion by the author. Instantiate Open Mist to complement Open Fog + Open Cloud.
Siamese Networks – Paraphrase Detection
Siamese Networks – Paraphrase Detection
T
This
Thin
is a
bags
plastic
may be
harmless
bag
to vehicle
Siamese Networks – Paraphrase Detection
T
This
is a
plastic
bag
Attributes
Semantics
Semantics
Thin
Semantics
bags
may be
Semantics
harmless
Semantics
to vehicle
Connected Vehicle
Mist Computing Tool
Support Vector Machine
SVM distinguishes gazelles, ostrich,
trees and ground in Namibia, Africa
www.cs.toronto.edu/~hinton/csc2515/notes/lec10svm.ppt
www.epfl.ch ▪ Patrick Meier at www.qcri.com
Support Vector Machines for ITS in China
5G
Latency 2ms
Jitter ??
Semantics
of
Time
Edge
Intelligence
(CNN, RNN)
Real-Time Convergence
V2V Intersection Movement Assist Warning Scenario
DSRC APPLICATIONS
PRIVATE
PUBLIC SAFETY
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APPROACHING EMERGENCY VEHICLE (WARNING) ASSISTANT (3)
EMERGENCY VEHICLE SIGNAL PREEMPTION
ROAD CONDITION WARNING
LOW BRIDGE WARNING
WORK ZONE WARNING
IMMINENT COLLISION WARNING (D)
CURVE SPEED ASSISTANCE [ROLLOVER WARNING] (1)
INFRASTRUCTURE BASED – STOP LIGHT ASSISTANT (2)
INTERSECTION COLLISION WARNING/AVOIDANCE (4)
HIGHWAY/RAIL [RAILROAD] COLLISION AVOIDANCE (10)
COOPERATIVE COLLISION WARNING [V-V] (5)
GREEN LIGHT - OPTIMAL SPEED ADVISORY (8)
COOPERATIVE VEHICLE SYSTEM – PLATOONING (9)
COOPERATIVE ADAPTIVE CRUISE CONTROL [ACC] (11)
VEHICLE BASED PROBE DATA COLLECTION (B)
INFRASTRUCTURE BASED PROBE DATA COLLECTION
INFRASTRUCTURE BASED TRAFFIC MANAGEMENT – [DATA COLLECTED
from] PROBES (7)
TOLL COLLECTION
TRAFFIC INFORMATION (C)
TRANSIT VEHICLE DATA TRANSFER (gate)
TRANSIT VEHICLE SIGNAL PRIORITY
EMERGENCY VEHICLE VIDEO RELAY
MAINLINE SCREENING
BORDER CLEARANCE
ON-BOARD SAFETY DATA TRANSFER
VEHICLE SAFETY INSPECTION
DRIVER’S DAILY LOG
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ACCESS CONTROL
DRIVE-THRU PAYMENT
PARKING LOT PAYMENT
DATA TRANSFER / INFO FUELING (A)
–
ATIS DATA
–
DIAGNOSTIC DATA
–
REPAIR-SERVICE RECORD
–
VEHICLE COMPUTER PROGRAM UPDATES
–
MAP and MUSIC DATA UPDATES
–
VIDEO UPLOADS
DATA TRANSFER / CVO / TRUCK STOP
ENHANCED ROUTE PLANNING & GUIDANCE (6)
RENTAL CAR PROCESSING
UNIQUE CVO FLEET MANAGEMENT
DATA TRANSFER / TRANSIT VEHICLE (yard)
TRANSIT VEHICLE REFUELING MANAGEMENT
LOCOMOTIVE FUEL MONITORING
DATA TRANSFER / LOCOMOTIVE
ATIS - Advanced Traveler Information Systems
CVO - Commercial Vehicle Operations
EV - Emergency Vehicles
IDB - ITS Data Bus
THRU – Through
V-V – Vehicle to Vehicle
(#) – Applications Submitted by GM/Ford/Chrysler
(A- Z) – Applications Submitted by Daimler-Chrysler
Basic Safety Message - SAE J2735 ● www.sae.org/standardsdev/dsrc/
5G
Latency 2ms
Jitter ??
Semantics
of
Time
Edge
Intelligence
(CNN, RNN)
Without time synchronization (time-centric software)
the promise of 5G may be limited in execution of CPS
Failure of
abstraction
Semantics of
time absent from
instruction set
architecture (ISA)
Prof Edward A Lee, UC Berkeley
37
Autonomous Vehicle
Medical Device
Prof Edward Lee, UC Berkeley ● www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-8.html
Essentially these are all sensor / communication networks
Cloud
Mist
Fog
Neonatal ICU at Oulu Health Labs
http://bit.ly/OULU-HEALTH-2015-CNN
Single Incision Laparoscopic Surgery
DOI 10.1109/TMECH.2015.2506148.IEEE/ASME
WSN In-Network Processing
Query
Result
www.cs.colorado.edu/~rhan/CSCI_7143_001_Fall_2003/agr24.ps
WSN In-Network Processing
WSN In-Network Processing
EDGE
INTELLIGENCE
EYERISS
Convolutional Neural Networks – At the Edge
http://arxiv.org/pdf/1411.4555v1.pdf
To translate languages, Recurrent Neural Network (RNN) transforms a French sentence into a vector representation, and a
second RNN uses that vector representation to generate a target sentence in German. Replace first RNN and input words
with deep Convolutional Neural Network (CNN) trained to classify objects in images and add known classes of objects in
semantic baffles with corresponding behavior (plastic bag versus wooden plank) with assigned probability of object in the
image (environment). Feed CNN’s rich encoding of the image into a RNN designed to produce phrases. We can then train the
whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match
the training descriptions for each image. The natural language spoken by human (inside vehicle) better trains the algorithms.
Author’s idea is adapted from → http://googleresearch.blogspot.co.uk/2014/11/a-picture-is-worth-thousand-coherent.html
5G
Latency 2ms
Jitter ??
Semantics
of
Time
Edge
Intelligence
(CNN, RNN)
Real-Time Convergence
Transportation Coordination - Emergency “Crash to Care” Response
Transportation of real-time data key to emergency search and rescue drones
US Park
Service
Drones
US DHS
Drone
US Fire
Service
Drone
Time, Sensors, Network
& Data Synchronization
Dhananjay Anand, NIST
5G in other modes of transportation
RAIL ENGINE
On-Board 5G
5850-5.925 GHz
Multi-Application OBU
(360 deg horizontal pattern)
Built-in and connected to an SAE1708, 1939, or EIA-709 LonWorks
interface bus
Interface Devices
(Built-in Display, Annunciator, Microphone, Keypad, etc.
connected to SAE-1708, 1939 or EIA-709 LonWorks interface bus)
Freight train arrives in Tehran (Feb 15, 2016) after travelling 14 days from China
www.scmp.com/comment/insight-opinion/article/1913441/chinas-new-silk-road-designed-cut-russia-out-eurasian-trade
Data is the new coal
US $1
Artificial Intelligence of Autonomous Driving
BILLION
56
4 SEP 2015
Emerging Frontier
Data Curation – sorting out what we need
CURATE
MONEY
Innovation in Curation Algorithms
How to extract ambient intelligence?
Postgres
Introduced the object-relational
model, effectively merging DB with
abstract complex data types eg
CAD, geospatial, so-called big data
www.csail.mit.edu/node/2459
32-G922 CSAIL MIT on 9th April 2015
Photograph taken by Shoumen Datta
Michael Stonebraker
Turing Award 2015
Challenges in Data Curation
● Noise obscures signal
● Data acquired is a blend of noise with signal
● Signal volatility introduces noise which is often proportional to signal
→ How do we correct/reduce the error due to this “noisy channel” factor?
→ Can novel algorithms reduce/deconstruct data to subtract “noise” and reconstruct the signal?
→ What about the application of the principles of (Shannon, Kalman-Bucy) error correcting algorithms?
 https://en.wikipedia.org/wiki/Kalman_filter
 http://news.mit.edu/2010/explained-shannon-0115
 http://www.cs.cmu.edu/~guyb/realworld/errorcorrecting.html
 http://www.cs.cmu.edu/~aarti/Class/10704/lec16-shannonnoisythrm.pdf
Data Curation Concepts from Laminar Flow
Data is not an unique issue in this context. This is applicable across all data types and domains.
This is a data curation problem. We are observing related signal/noise issues in big data analytics.
Are there any concepts related to data curation which may be triggered by laminar flow? Watch:
http://bit.ly/LAMINAR-FLOW-DATA-CURATION-CONCEPT
Data Saves Lives
2.5 million falls 2013
734,000 hospitalized
25,500 died from fall
$34 billion direct cost
66
Professor Dina Katabi (MIT) presenting RF Reflection to
President Obama (White House Demo, 4 August 2015)
WHITE HOUSE VIDEO ● http://bit.ly/President-Obama-with-Dina-Katabi
http://newsoffice.mit.edu/2015/president-obama-meets-mit-entrepreneurs-white-house-demo-day-0806
You can’t build an elephant
using the mouse as a model
The pursuit of “large scale” test beds and deployments
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Young’s Double Slit Experiment with Electrons
Dr. Akira Tonomura, Hitachi Research Laboratories, 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
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http://blogs.scientificamerican.com/guest-blog/what-does-the-new-double-slit-experiment-actually-show/
slit
double slit
Content restricted to IIC Members
Not for External Publication
73
Data is the new coal
Autonomy and Algorithms are essential catalysts
The business model of communication is inextricably linked to service.
http://gsacom.com/wp-content/uploads/2015/11/151118-GSA-the-Road-to-5G.pdf
For more information
http://bit.ly/MIT-IOT
This URL will take you to my MIT Libraries Dspace page at MIT
Please scroll down the page (long list of items) to the bottom
Find 5G-MWC - zipped folder with key references and papers
Find 5G-MWC-TALK - this presentation (a complete copy)
Find REVIEW-IOT if you are interested in the big picture
Dr Shoumen Datta ● shoumen@mit.edu
Dr Shoumen Datta ● datta@iiconsortium.org
Thank you
I have created nothing new
This document suggests ideas which are neither original nor the outcome of the
author’s research or creativity. The synthesis of existing facts and weaving them
to provoke dialogue may be attributed to the author. The author has no claim or
rights over the data, visuals and graphics used in this document. The material is
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The comments and opinions are solely due to the author. (shoumen@mit.edu)