Data and Society Lecture 3: Big Data 2 -- IoTbermaf/Data Course 2018/Lecture 3 --Big Data 2.pdf•...

Fran Berman, Data and Society, CSCI 4370/6370

Data and Society

Lecture 3: Big Data 2 -- IoT

2/2/18


Announcements 2/2

• Office Hours: Friday 1-2 (AE 218) or by

appointment (send email to [email protected])

• NO CLASS NEXT WEDNESDAY, CLASS FRIDAY

• Op-Ed draft due February 9 – instructions in Lecture

1. Please turn in hardcopy to Fran at the beginning

of class.


Wednesday Section Friday lecture

First Half of Class Second Half of Class Assignments

January 17 : NO class January 19 L!: CLASS INTRO AND LOGISTICS Presentation Model / Op-Ed Instructions

Op-Ed instructions

January 24: NO class January 26 L2: BIG DATA 1 4 Presentations

January 31: NO class February 2 L3: BIG DATA 2 -- IoT 4 Presentations

February 7: NO class February 9 L4: DATA AND SCIENCE 4 Presentations Op-Ed due Feb. 9

February 14: 5 Presentations

February 16 L5: DATA AND HEALTH / LESLIE McINTOSH GUEST SPEAKER

4 Presentations Op-Ed drafts returned Feb. 21


February 23 L6: DATA STEWARDSHIP AND PRESERVATION

4 Presentations Research Paper instructions


March 2 L7: DATA INFRASTRUCTURE 4 Presentations Op-Ed Final due March 2

March 7 : 5 Presentations March 9: NO CLASS / PAPER PREPARATION

March 14: Spring Break March 16 SPRING BREAK

March 21: NO class March 23: NO CLASS / PAPER PREPARATION

March 28: 5 Presentations

March 30 L8: DATA RIGHTS, POLICY, REGULATION 4 Presentations Research Paper due March 28

April 4: NO class April 6 L9: DATA AND ETHICS 4 Presentations

April 11: 5 Presentations April 13 L10: DATA AND COMMUNICATION 4 Presentations

April 18: 5 Presentations April 20 L10: DATA FUTURES 4 Presentations

April 25: 5 Presentations April 27 L11: HOT TOPICS / TBD


Lecture 2: Big Data 2


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.”

Internet of Things - Scale

Devices connected to the Web:

• 1970 = 13

• 1980 = 188

• 1990 = 313,000

• 2000 = 93,000,000

• 2010 = 5,000,000,000

• 2020 = 31,000,000,000

Source: IntelSlide courtesy of Chris Greer, NIST


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.

• 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


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 IoTsecurity 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

https://www.forrester.com/TechRadar+Internet+Of+Things+Q1+2016/fulltext/-/E-res121873

http://www.forbes.com/sites/gilpress/2016/01/27/internet-of-things-iot-predictions-from-forrester-machina-research-wef-gartner-idc/#70b156a86be6


Infrastructure Challenges

• Interoperability and composability among heterogeneous devices, components and systems

• Need for reference architectures, shared vocabularies and standards that allow diverse “things” to interact

• Development of systems and approaches that can adapt to emergent behaviors, unintended consequences, re-purposing, multiple environments.

• Development of adequate mechanisms for security, privacy, and trust in components and systems


Infrastructure Challenges

• Support for stewardship and use of data from multiple components, used by multiple systems, and useful in multiple scenarios (many “v’s”!).

• 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

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


Challenges for the Enterprise

(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?


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


• Remotely monitor and manage your home / physical environments

• Promote efficient and cost-effective resource usage



The Future is

Here: Urban Areas


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



The Future is

Here: Business


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

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



The Future is

Here: Ecosystems


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



The Future is

Here: Urban Areas


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



Smart Transportation: 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


Autonomous vehicles: Many opportunities

and challenges

• Autonomous vehicles can transform transportation

– Contribute to safer highways and fewer accidents

– Expand transportation options for elderly, children, delivery, etc.

– Optimize trips by better coordination with other vehicles, choice of routes, response to weather and unanticipated situations

• Autonomous vehicles must operate autonomously in unpredictable environments

– Systems must work robustly

– Systems must be programmed or learn to anticipate new environments with good responses

– Systems and devices must coordinate smoothly

– Regulation and policy must be developed to reasonably attribute responsibility and accountability, etc.


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

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


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



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?


Legislation currently being

developed for autonomous vehicles

http://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_Legislative_and_Regulatory_Action

http://www.roboticstomorrow.com/news/2016/04/06/ieee-standards-association-introduces-global-initiative-for-ethical-considerations-in-the-design-of-autonomous-systems/7917/

States with bills about self-driving vehicles


Self-driving cars: Safety

• What can go wrong?

– Sensors don’t function correctly (GPS does not have a clear view of sky, cameras don’t have enough light, radar not accurate, etc.)

– Vehicle encounters situation not anticipated by the SW, including anything out of the ordinary done by other drivers.


How safe is safe enough?

• NTSA: driverless-vehicle fleet should increase safety at least two-fold to earn government approval (2017) – cut in half the current toll of 40,200 highway deaths annually

• How much risk is OK?

– Risk of dying in a car crash on a given trip: 1 in 7M

– Risk of dying in a car crash over a “lifetime”: 1 in 114

– Risk of dying in a plane crash (largely autonomous) over a lifetime: 1 in 9821 – will this be good enough for cars?

NTSA model for reasons behind crashes based on 5470 crashes from 2005-2007, https://www.caranddriver.com/features/autonomous-cars-how-safe-is-safe-enough-feature

https://www.caranddriver.com/features/autonomous-cars-how-safe-is-safe-enough-feature


Ted talk: How a self-driving car sees the

road

• https://www.ted.com/talks/chris_urmson_how_a_driverless_car_sees_the_road#t-70424(15 minutes)

https://www.ted.com/talks/chris_urmson_how_a_driverless_car_sees_the_road#t-70424


Modeling Risk (Rand paper) – when is the best

time to release autonomous technologies?

• Scenario 1:

– Autonomous vehicles are 10% safer than human drivers

– Some consumers purchase in 2020, AVs account for 80% of miles traveled by 2060

• Scenario 2:

– Roll out autonomous vehicles when they are “nearly perfect” in 2040

– By 2070, autonomous vehicles account for 80% of miles traveled


How many accidents are acceptable?

• 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, https://www.theguardian.com/science/2016/jun/14/statistically-self-driving-cars-are-about-to-kill-someone-what-happens-next

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

https://www.theguardian.com/science/2016/jun/14/statistically-self-driving-cars-are-about-to-kill-someone-what-happens-next


Lecture 7 Sources• “IoT Predictions from Forrester, Machina Research, WEF, Gartner, IDC”, Forbes, January 27, 2016,


• “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

• 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

• “Redefining Safety for Self-Driving cars”, Scientific American, https://www.scientificamerican.com/article/redefining-ldquo-safety-rdquo-for-self-driving-cars/

• “Autonomous Cars: How safe is safe enough”, Car and Driver, https://www.caranddriver.com/features/autonomous-cars-how-safe-is-safe-enough-feature

• “Why Waiting for Perfect Autonomous Vehicles May Cost Lives,” Rand Corporation, https://www.rand.org/blog/articles/2017/11/why-waiting-for-perfect-autonomous-vehicles-may-cost-lives.html


https://hbr.org/2015/10/how-smart-connected-products-are-transforming-companies

http://www.emc.com/leadership/digital-universe/2014iview/index.htm

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


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

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

https://www.scientificamerican.com/article/redefining-ldquo-safety-rdquo-for-self-driving-cars/

https://www.caranddriver.com/features/autonomous-cars-how-safe-is-safe-enough-feature

https://www.rand.org/blog/articles/2017/11/why-waiting-for-perfect-autonomous-vehicles-may-cost-lives.html


Discussion Article for Next Week (February 9)

• “What will our lives be like as Cyborgs?”, The Atlantic, https://www.theatlantic.com/technology/archive/2017/10/cyborg-future-artificial-intelligence/543882/

https://www.theatlantic.com/technology/archive/2017/10/cyborg-future-artificial-intelligence/543882/


Discussion

• “I had my DNA Picture Taken with Varying Results”, New York Times, http://www.nytimes.com/2013/12/31/science/i-had-my-dna-picture-taken-with-varying-results.html

http://www.nytimes.com/2013/12/31/science/i-had-my-dna-picture-taken-with-varying-results.html


Break


Presentation Articles for February 9

• “The LSST and Big Data Science”, Astronomy,

http://www.astronomy.com/news/2017/12/the-lsst-and-big-data-science (Alex C)

• “On Long Migrations, Birds Chase an Eternal Spring”, New York Times,

https://www.nytimes.com/2017/01/05/science/on-long-migrations-birds-chase-an-

eternal-spring.html (Ethan G)

• “Rhinocerous DNA Database Successful in Aiding Poaching Prosecutions”, The

Guardian, https://www.theguardian.com/science/2018/jan/08/rhinoceros-dna-

database-successful-in-aiding-poaching-prosecution (Sarah M)

• “What Is Multispectral Imaging And How Is It Changing Archaeology And Digital

Humanities Today?”, Forbes,

https://www.forbes.com/sites/drsarahbond/2017/11/30/what-is-multispectral-

imaging-and-how-is-it-changing-archaeology-and-digital-humanities-

today/#30ce91cf5151 (Tae P)

http://www.astronomy.com/news/2017/12/the-lsst-and-big-data-science

https://www.nytimes.com/2017/01/05/science/on-long-migrations-birds-chase-an-eternal-spring.html

https://www.theguardian.com/science/2018/jan/08/rhinoceros-dna-database-successful-in-aiding-poaching-prosecution

https://www.forbes.com/sites/drsarahbond/2017/11/30/what-is-multispectral-imaging-and-how-is-it-changing-archaeology-and-digital-humanities-today/#30ce91cf5151


Presentation Articles for February 14

• “100,000 IoT sensors monitor a 1400 kilometer canal in China,” IEEE Spectrum, https://spectrum.ieee.org/tech-talk/telecom/internet/a-massive-iot-sensor-network-keeps-watch-over-a-1400kilometer-canal [Yishan D]

• “Gifts that snoop? The Internet of Things is wrapped in privacy concerns,” Consumer Reports, https://www.consumerreports.org/internet-of-things/gifts-that-snoop-internet-of-things-privacy-concerns/ [Tim W]

• “Can the U.S. Senate secure the Internet of Things?”, Network world, https://www.networkworld.com/article/3217384/internet-of-things/can-the-us-senate-secure-the-internet-of-things.html [Lindsay Z]

• “How the end of Net Neutrality will affect the Internet of Things”, Network World, https://www.networkworld.com/article/3244251/net-neutrality/how-the-end-of-net-neutrality-will-affect-iot.html [Trulee H]

https://spectrum.ieee.org/tech-talk/telecom/internet/a-massive-iot-sensor-network-keeps-watch-over-a-1400kilometer-canal

https://www.consumerreports.org/internet-of-things/gifts-that-snoop-internet-of-things-privacy-concerns/

https://www.networkworld.com/article/3217384/internet-of-things/can-the-us-senate-secure-the-internet-of-things.html

https://www.networkworld.com/article/3244251/net-neutrality/how-the-end-of-net-neutrality-will-affect-iot.html


Presentation Articles for Feburary 16

• “Relative risk of Alzheimer’s between men and women: Record corrected”, Science Daily, https://www.sciencedaily.com/releases/2017/08/170828124531.htm [Sam S-F]

• “How Big Tech is Going After your Health Care,” NY Times, https://www.nytimes.com/2017/12/26/technology/big-tech-health-care.html[Daniel C]

• “Healthcare is hemorrhaging data. AI is here to help.”, Wired, https://www.wired.com/story/health-care-is-hemorrhaging-data-ai-is-here-to-help/ [Matthew M]

• “Researchers use WWII Code-breaking Techniques to Interpret Brain Data,” Phys. Org., https://phys.org/news/2017-12-wwii-code-breaking-techniques-brain.html[Chandler M]

https://www.sciencedaily.com/releases/2017/08/170828124531.htm

https://www.nytimes.com/2017/12/26/technology/big-tech-health-care.html

http://www.nytimes.com/2016/12/23/health/cancer-trials-immunotherapy.html?rref=collection/seriescollection/immunotherapy

https://phys.org/news/2017-12-wwii-code-breaking-techniques-brain.html


Presentation Articles for Today

• “Rethinking Storage in the Age of Big Data”, ComputerWeekly.com, http://www.computerweekly.com/feature/Rethinking-storage-in-the-age-of-big-data (Jiyu H)

• “Google Flu Trends: The Limits of Big Data”, New York Times, http://bits.blogs.nytimes.com/2014/03/28/google-flu-trends-the-limits-of-big-data/ (Griffin M)

• “The Shazam Effect”, The Atlantic, http://www.theatlantic.com/magazine/archive/2014/12/the-shazam-effect/382237/ (Madison W)

• “Giving Viewers What They Want,” NY Times, http://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?pagewanted=all&_r=1&(Reilly K)

http://www.computerweekly.com/feature/Rethinking-storage-in-the-age-of-big-data

http://bits.blogs.nytimes.com/2014/03/28/google-flu-trends-the-limits-of-big-data/

http://www.theatlantic.com/magazine/archive/2014/12/the-shazam-effect/382237/

http://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?pagewanted=all&_r=1&

Data and Society Lecture 3: Big Data 2 -- IoTbermaf/Data Course 2018/Lecture 3 --Big Data 2.pdf•...

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Transcript of Data and Society Lecture 3: Big Data 2 -- IoTbermaf/Data Course 2018/Lecture 3 --Big Data 2.pdf•...