Data and Society Lecture 4: Future of Infrastructure –

Internet of Things

2/19/16

Announcements

• Exam next week – February 26

– Exam will cover Lectures 1-4.

– Exam will be essay.

– Students are responsible for the material discussed in lectures

• Paper Assignment discussed next week

• L4 Roundtable next week

Today (2/19/16)

• Any questions?

• Lecture 4: Future of Infrastructure – Internet of Things

– Internet of things applications and infrastructure

– Self-driving cars

– Quantified self

• L3 Data Roundtable

3

Section Theme Date First “half” Second “half”

Section 1: The Data Ecosystem -- Fundamentals

January 29 Class introduction; Digital data in the 21st Century (L1)

Data Roundtable / Fran

February 5 Data Stewardship and Preservation (L2)

L1 Data Roundtable / 5 students

February 12 Data-driven Science (L3) L2 Data Roundtable / 5 students

February 19 Future infrastructure – Internet of Things (L4)

L3 Data Roundtable / 5 students

February 26 Section 1 Exam L4 Data Roundtable / 5 students

Section 2: Data and Innovation – How has data transformed science and society?

March 4 Paper assignment description Section 1 Data Roundtable / 5 students

March 11 Data and Health: Phil Bourne guest lecture (L5)

Section 2 Data Roundtable / 5 students

March 18 Spring Break / no class

March 25 Data and Entertainment (L6) L5 Data Roundtable / 5 students

April 1 Big Data Applications (L7) L6 Data Roundtable / 5 students

Section 3: Data and Community – Social infrastructure for a data-driven world

April 8 Data in the Global Landscape (L8) Section 2 paper due

L7 Data Roundtable / 5 students

April 15 Digital Rights (L9) L8 Data Roundtable / 5 students

April 22 Bulent Yener Guest Lecture, Data Security (L10)

L9 Data Roundtable / 5 students

April 29 Digital Governance and Ethics (L11) L10 Data Roundtable / 5 students

May 6 Section 3 Exam L11 Data Roundtable / 5 students

We are here

Lecture 4: Future of Infrastructure – Internet of Things

The Internet of Things (IoT)

• Wikipedia: “The Internet of Things is the network of physical objects of “things” embedded with electronics, software, sensors and connectivity to enable it to achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure.”

Impact

• IoT expected to

– Usher in automation in all fields

– Enable advanced “smart” applications. In particular, smart wearables, smart home, smart city, smart environment, smart enterprise.

– Converge multiple technologies including wireless, embedded systems, micro-electromechanical systems (MEMS), small and large-scale devices, etc.

One of the first Internet appliances: the CMU Coke

Machine, circa 1982. (Machine could report its inventory and

whether drinks were cold.)

Image from http://www.smartid.it/en/machin

e-machine-m2m

Basics

• Expected to be 26-30B devices on the Internet by 2020

– Internet of “objects” even larger (50-100 trillion objects). Estimated that human beings in urban environments each surrounded by 1000-5000 track-able objects.

• Each device will need a unique IP address.

– IPv4 only allows for 4.3B unique addresses, will not support IoT

– IPv6 needed. Global adoption of IPv6 critical to support IoT.

• Web of Things will provide an application layer of the Internet of Things

• Many IoT solutions presuppose the ability to gather / store / mine data efficiently and in real-time …

IoT Technology Roadmap

Opportunities

• Customization

– IoT provides the opportunities to gather data and developed customized solutions.

• This includes the ability to target customers specifically in terms of what they like, what they want, what they are willing to pay, etc.

• Monitoring

– IoT provides the opportunity to monitor and assess systems and environments, with the potential to more accurately predict risk

• Paradigm shift

– IoT will provide the ability to shift our approach in many areas to encompass more dynamic, real-time solutions.

– For example, smart manufacturing technologies transforming the conventional “Produce Store Acquire” to “Acquire Produce”, benefitting producer and consumer.

Opportunities • Smart Automation

– IoT can provide the ability for precision control of environments and systems. Home automation, precision farming, advanced manufacturing, Watson, etc. mean that some of the conceptual tasks are shared between human and machine

• Adaptive systems

– Ability to gather and process information in real-time provides the opportunity to modify behavior and create adaptive systems that respond to dynamic phenomena and promote efficiency

Image from http://www.nbcnews.com/tech/innovation/can-l-kill-traffic-self-driving-cars-n217211

Smart Cities

• Santander, Spain – 180,000 inhabitants, smartphone app connected to 10K sensors

enabling parking search, environmental monitoring, digital city agenda, etc.

– Image from http://www.spiegel.de/fotostrecke/photo-gallery-santander-the-smart-city-fotostrecke-94339-4.html

• Songdo, South Korea -- Goal is to build fully equipped and wired smart city in which almost everything is connected and monitored by machine

• San Jose, California – using IoT to improve air and water quality, reduce noise pollution, increase transportation efficiency

• New York City – IoT being used to connect vessels and monitor NY Waterway -- Hudson River, East River, Upper NY Bay

The Future is Here:

Sensors and Connectivity

IoT-focused products

• help monitor/promote your health

• Better care for those whose health you’re responsible for

From http://postscapes.com/internet-of-things-examples/

The Future is Here:

Home / Physical

Environments

IoT-focused products

• Remotely monitor and manage your home / physical environments

• Promote efficient and cost-effective resource usage

From http://postscapes.com/internet-of-things-examples/

The Future is Here:

Urban Areas

IoT-focused products

• Use real-time data and adaptive systems to promote the health, safety, security, and well-being of citizens

From http://postscapes.com/internet-of-things-examples/

The Future is Here:

Business

IoT-focused products

• provide new tools for boosting productivity, optimizing operations, saving resources and costs

• provide new ways of engaging with the customer and creating competitive advantage

From http://postscapes.com/internet-of-things-examples/

The Future is Here:

Ecosystems

IoT-focused products

• Use real-time data and predictive analysis to better understand and manage ecosystems and natural resources

From http://postscapes.com/internet-of-things-examples/

The Future is Here:

Urban Areas

IoT-focused products

• Use real-time data and adaptive systems to promote the health, safety, security, and well-being of citizens

From http://postscapes.com/internet-of-things-examples/

Forrester: IoT “technologies are diverse and immature.”

• “Standards are nascent, as vendors are only a couple of years into the process of creating general-purpose interoperability standards. And IoT security technologies are still in the Creation phase, with no established products.”

• Source: "TechRadar™: Internet Of Things, Q1 2016, Forrester Research" (https://www.forrester.com/TechRadar+Internet+Of+Things+Q1+2016/fulltext/-/E-res121873 ) as described in Forbes (http://www.forbes.com/sites/gilpress/2016/01/27/internet-of-things-iot-predictions-from-forrester-machina-research-wef-gartner-idc/#70b156a86be6 [also source of graphic])

Used by permission

Infrastructure Challenges

• Development of feasible data infrastructure for handling, storage, tracking of heterogeneous data sources

• Development of efficient algorithms for discovery, data mining, analysis for data at massive time and spatial scales

• Governance, regulation of human-autonomous systems; community standards, policy, practice

• Privacy, autonomy, control (manifesting in issues with user consent, freedom of choice, anonymity, undue exposure of minors, etc.)

• Data and systems security

• Environmental impact of contamination due to dumping of IoT devices, sensors, etc., cost of mining rare-earth metals (used in modern electronic components)

IoT Infrastructure Implications 1 (from IDC IoT report)

• Information security will need to be improved

• Information privacy will need to be ensured, particularly as more third-party consumer data is incorporated into the operational and analytical frameworks

• Data warehouses will need to be upgraded or swapped out for more flexible data repositories that can handle various data types, automatic tagging, autonomous data check in, etc.

• Data analytic output will need to be driven to more parts of the organization, including real-time input to operational decision making

IoT Infrastructure Implications 2 (from IDC IoT report)

• Datacenter will have to be virtualized and cloud computing incorporated into enterprise IT architectures.

• IT managers will have to enable and mange the explosive growth in mobile devices that request and send data around the world.

• IT managers will need to provide the appropriate security permissions to allow for data to be queried regardless of where it is stored

• As data used increasingly for decision making, integrity, organization, access, interoperability, etc. increasingly needed for useful results

IoT Infrastructure Implications – Choices and Challenges (from HBR)

• Which set of smart, connected product capabilities and features should a company pursue?

• How much functionality should be embedded in the product and how much in the cloud?

• Should the company pursue and open or closed system?

• Should the company develop the full set of smart, connected product capabilities and infrastructure internally or outsource to vendors and partners (and what are the repercussions)?

• What data must the company capture, secure, and analyze to maximize the value of its offering?

• How does the company manage ownership and access rights to its product data?

Infrastructure implications: Self-driving cars

• An autonomous (driverless, self-driving) car is a vehicle that is capable of sensing its environment and navigating without human input.

• Autonomous cars use radar, lidar, GPS, Odometry, Computer vision, etc. to sense their environment.

• Control systems interpret sensory information to identify appropriate navigation paths, obstacles, relevant signage, different cars on the road, etc.

"Jurvetson Google driverless car trimmed" by Flckr user jurvetson (Steve Jurvetson). Trimmed and retouched with PS9 by Mariordo -

http://commons.wikimedia.org/wiki/File:Jurvetson_Google_driverless_car.jpg. Licensed under CC BY-SA 2.0 via Commons - https://commons.wikimedia.org/wiki/File:Jurvetson_Google_driverless_car_trimmed.jpg#/media/File:Jurvetson_Google_driverless_car_trimmed.jpg

What Level of Automation? Report from the Society of Motor Manufacturers and Traders (SMMT) in the U.K. https://www.kpmg.com/BR/en/Estudos_Analises/artigosepublicacoes/Documents/Industrias/Connected-Autonomous-Vehicles-Study.pdf

• L0: Driver only

• L1: Assisted

• L2: Partial automation (driver must monitor driving and environment at all times)

• L3: Conditional automation (driver does not need to monitor driving and environment; driver must be in a position to resume control)

• L4: High automation (driver not required during defined use case)

• L5: Full automation

Car-net Technology roadmap from Society of Motor Manufacturers and Traders (SMMT) in the U.K. https://www.kpmg.com/BR/en/Estudos_Analises/artigosepublicacoes/Documents/Industrias/Connected-Autonomous-Vehicles-Study.pdf

• L0: Driver only

• L1: Assisted

• L2: Partial automation (driver must monitor driving and environment at all times)

• L3: Conditional automation (driver does not need to monitor driving and environment; driver must be in a position to resume control)

• L4: High automation (driver not required during defined use case)

• L5: Full automation

New directions for commercial interests

• Nissan, BMW, Tesla, Audi, Mercedes, etc. – targeting semi-autonomous or autonomous cars

– Parts suppliers Delphi and Bosch targeting autonomous vehicles

• Vehicle internet efforts will exchange information between cars in the same vicinity and cars and traffic management systems

https://www.youtube.com/watch?v=1OPESlV1sQA

Safety • Vehicles perform admirably under ideal conditions but run into more

problems on unfamiliar routes and in rough weather.

– Highway driving working well, city driving more challenging

• Some data on performance:

– Google: Between 9/14 and 11/15, Google cars experienced 272 technology failures and would have crashed at least 13 times if drivers had not intervened.

• (49 cars, 424K autonomous miles, 341 needed “disengagements” where cars handed control back to test drivers or drivers intervened).

• [http://www.theguardian.com/technology/2016/jan/12/google-self-driving-cars-mistakes-data-reports]

– NHTSA studies indicate that vehicular communication systems (where vehicles and roadside units communicate in a peer-to-peer network) could help avoid up to 79% of all traffic accidents.

How much “human in the loop”? • Should cars be fully driverless?

– Mindell: Experience with aircraft, underwater exploration, air travel, etc. point to “no”

• Examples from the Apollo program, semi-automated commercial aircraft, etc.

• “Airline pilots are constantly making small corrections, picking up mistakes, correcting the air traffic controllers.”

– Human in the loop important for critical and unexpected situations.

• Full autonomy vs “trusted, transparent, reliable, safe autonomy that is fully interactive: The car does what I want it to do, and only when I want it to do it.”

• Mindell: on a scale from 1 to 10, automation should be at level 5.

– http://news.mit.edu/2015/no-driverless-cars-1013

Policy and regulation • National Highway Traffic Safety Administration (NHTSA)

working with automakers and state governments to develop prototype laws, regulations, performance metrics, testing methods, etc.

– Who is accountable if a self-driving car hits someone?

– What should we do when self-driving cars are hacked?

– How should the computer decide between two bad options (e.g. hitting a tree or running over a pedestrian)?

– What policy / regulation is needed in a “car –net” environment where vehicles communicate with one another?

– How should we regulate autonomous cars’ impact on the ecosystem?

The Quantified Self

• Wikipedia: “The Quantified Self is a movement to incorporate technology into data acquisition on aspects of a person’s daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical). … “

• Primary approach is the collection and analysis of data generated by sensors, instruments, apps, and manually

• Quantified self technologies providing unprecedented detail about function and performance.

• Used increasingly for to monitor / assess health and productivity

From: http://www.health2con.com/news/2011/

11/22/taking-quantified-self-to-the-classroom/

The Quantified Employee

• Focus of the Quantified Employee movement to is increase productivity employee and other data. New technologies being used to sense many aspects of work and performance.

• Employee monitoring tools can provide data on:

– Employee internet access (instrumented machines, devices)

– Employee activity (keystroke logging, screen recording)

– Employee productivity (e.g. lines of code)

– Employee health and happiness (via wearables, sensors, apps)

– Employee location (instrumented badge)

– Behavior (surveillance)

– Communication and collaboration (through To:/cc/From: on emails)

Dilbert on the Quantified Employee

From: http://travelinlibrarian.info/2014/05/dilbert-dark-side-quantified-self/

More Productivity or Big Brother?

Challenges for employers looking to increase employee performance through data:

1. Getting meaningful data, and only meaningful data. Employers need to be able to capture and organize information in a way that can actually lead to insights. Work data is generally unstructured, and fragmented across many systems. Pulling the right data into a single repository is challenging.

2. Getting insight from data. What are the real cause-and-effect levers that cause some people to be so much more productive than others? How do you weed out spurious correlations? Many big data initiatives fail because value can't be found in the data.

3. Driving change from insight. Employers need to find ways to actually get employees to take action based on the insights. Otherwise, the data is useless. If no results can be derived from the data, the data becomes one more analytics dashboard without benefit.

From http://insights.wired.com/profiles/blogs/quantified-self-how-about-a-quantified-workplace

Quantified Health • Focus is data on you and how you can

use it to improve your life/lifestyle.

• Many devices, systems, apps available to monitor health and environment:

– Monitors of activity – e.g. steps taken, calories burned, body temperature, heart rate, sleep quality

– Monitors of well being – e.g. mood / energy, food and alcohol consumption, nutrition, behavior

– Systems to monitor / adapt your environment – e.g. lighting that is responsive to mood / stress level, bed / bedding that adapts to your needs

– Greater information about your biological system – e.g. information about your genes, microbiomes

Devices worn by Chris Dancy as reported in http://mashable.com/2014/08/21/most-connected-man/

• Chris Dancy famous for using 300-700 tracking and lifelogging (quantified self) systems at all times, from Fitbit to Beddit mattress cover.

Quantified Self: Larry Smarr at TedMed (16 min)

http://www.tedmed.com/talks/show?id=18018

IoT: Ethics Lecture 10

“ … there are broader ethical issues to consider. Where would the areas be where [IoT] devices would not be appropriate to use? What about our personal privacy, both in the collection of data about us, but also in the

way it’s combined and acted on?

How should the law deal with smart devices, if an incorrect decision is acted on, who or what is

responsible? There are many others. “

Peter McOwan, Professor of CS at Queen Mary, University of London.

From The Guardian

"The Circle is a genre novel, with its simplistic fabrication meant to be

obvious. The symbolism is abundantly clear, because it is Eggers' only way of

bringing his message to the ear: How do we mean to handle the right to

sovereignty of interpretation over one's own life in the future?“

Thomas Andre review in Der Spiegel

Lecture 4 Sources (not already on slides)

• “IoT Predictions from Forrester, Machina Research, WEF, Gartner, IDC”, Forbes, January 27, 2016, http://www.forbes.com/sites/gilpress/2016/01/27/internet-of-things-iot-predictions-from-forrester-machina-research-wef-gartner-idc/#70b156a86be6

• “How Smart, Connected Products are Transforming Companies,” Harvard Business Review, https://hbr.org/2015/10/how-smart-connected-products-are-transforming-companies

• “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things”, IDC report http://www.emc.com/leadership/digital-universe/2014iview/index.htm

• “Robots and us” MIT News, http://news.mit.edu/2015/no-driverless-cars-1013 • “Connected and Autonomous Vehicles – the UK Economic Opportunity”, SMMT Report,

https://www.kpmg.com/BR/en/Estudos_Analises/artigosepublicacoes/Documents/Industrias/Connected-Autonomous-Vehicles-Study.pdf

• Wikipedia, “Quantified Self”, “Internet of Things”, “The Circle”, http://en.wikipedia.org/wiki/Internet_of_Things • An Internet of Things, http://postscapes.com/internet-of-things-examples/ • “White House hopes to shape national policy on driverless cars”, Washington Post,

https://www.washingtonpost.com/local/trafficandcommuting/white-house-hopes-to-shape-national-policy-on-driverless-cars/2016/01/14/46e3bd1e-ba4e-11e5-829c-26ffb874a18d_story.html

• “Quantified Self: Meet the Quantified Employee,” Forbes, http://www.forbes.com/sites/joshbersin/2014/06/25/quantified-self-meet-the-quantified-employee/

• “These Companies are Tracking the Fitness of Their Employees”, The Guardian, http://www.theguardian.com/technology/2014/mar/17/why-companies-are-tracking-the-fitness-of-their-employees

• “Quantified Self? How about a Quantified Workplace?”, Wired, http://insights.wired.com/profiles/blogs/quantified-self-how-about-a-quantified-workplace

• “The Most Connected Man is You, Just a Few Years from Now”, Mashable, http://mashable.com/2014/08/21/most-connected-man

Data Roundtable

Next two weeks (March 4): Data Roundtable (Need volunteers)

• “Bloomberg: Six Gotchas that will Upend your Data Science Team,” TechTarget, January 27, 2016, http://searchcio.techtarget.com/tip/Bloomberg-Six-gotchas-that-will-upend-your-data-science-team (Aesa K.)

• “Voter Data Modeling: Does it threaten our privacy?” Government Technology, January 29, 2016, http://www.govtech.com/data/Voter-Data-Modeling-Does-it-Threaten-Our-Privacy.html (Craig D.)

• “How Big Data is Going to Help Feed 9 Billion People by 2050” TechRepublic, April 27, 2015, http://www.techrepublic.com/article/how-big-data-is-going-to-help-feed-9-billion-people-by-2050/ (Levin H.)

• “Apple vs. FBI is epic fight over privacy and national security, experts say”, L.A. Times, February 18, 2016, http://www.latimes.com/local/lanow/la-me-ln-apple-vs-fbi-is-epic-fight-over-privacy-national-security-20160218-story.html (Jon-Pierre H.)

• “Right Now, The Internet Of Things Is Like The Internet Of The 1990s”, Fast Company, March 27, 2015, http://www.fastcompany.com/3044375/sector-forecasting/the-future-of-the-internet-of-things-is-like-the-internet-of-the-1990s (Bo Z.)

Next Week: L4 Roundtable February 26 • “How the Internet of Things got hacked,” Wired, December, 2015,

http://www.wired.com/2015/12/2015-the-year-the-internet-of-things-got-hacked/ (Theo B.)

• “The Measured Life,” MIT Technology Review, June 21, 2011, http://www.technologyreview.com/featuredstory/424390/the-measured-life/ (Brenda T.)

• “GM and Lyft are building a network of self-driving cars,” Wired, January 4, 2016, http://www.wired.com/2016/01/gm-and-lyft-are-building-a-network-of-self-driving-cars/ (Chris P.)

• “Hijackers remotely kill a jeep on the highway – with me in it,” Wired, July 2, 2015, http://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/ (TK W.)

• “Robot doctors, online lawyers and automated architects: the future of the professions?”, The Guardian, June 15, 2014, http://www.theguardian.com/technology/2014/jun/15/robot-doctors-online-lawyers-automated-architects-future-professions-jobs-technology (Sri I.)

Today: L3 Data Roundtable for February 19

• “Scientists Say that a Neptune-Sized Planet Lurks Beyond Pluto,” Science, January 20, 2016, http://www.sciencemag.org/news/2016/01/feature-astronomers-say-neptune-sized-planet-lurks-unseen-solar-system (Kiana M.)

• “Birdwatchers help Science fill gaps in the Migratory Story,” NYTimes, January 30, 2016, http://www.nytimes.com/2016/01/29/science/bird-watchers-help-science-fill-gaps-in-the-migratory-story.html?rref=collection%2Fsectioncollection%2Fscience&action=click&contentCollection=science&region=rank&module=package&version=highlights&contentPlacement=8&pgtype=sectionfront (Amelia GB)

• “NCAR announces powerful new supercomputer for advanced atmospheric, geosciences modeling,” Scientific Computing, January 11, 2016, http://www.scientificcomputing.com/news/2016/01/ncar-announces-powerful-new-supercomputer-advanced-atmospheric-geosciences-modeling (Kienan K.)

• “Scientists Use Stargazing Technology in the Fight against Cancer,” Time, February, 2013 http://healthland.time.com/2013/02/27/scientists-use-stargazing-technology-in-the-fight-against-cancer/ (Courtney T.)

• “Digital Keys for Unlocking the Humanities’ Riches”, the New York Times, November 16, 2010, http://www.nytimes.com/2010/11/17/arts/17digital.html?pagewanted=all&_r=0 (Jessica J.)