— ITPG-GT 2621 001 CLOUDCOMMUTING

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The Game: 5 players representing different member of ITP community spread rumors by word-of-mouth over 3 days

Goal: better understand ITP communication patterns by tracking the spread of viral information and visualizing social cliques

Inspiration:

ITP List Serv Network Viz
ITP list serv visualization shows an imbalance in usage of ITP list serv (2nd year students use more frequently and mainly email other second year students) Also shows what students are “hubs” within the ITP community. Can the flow of information between the two classes of students be improved?

Zachary’s Karate Club Graph describes the friendships between the members of a US karate club in the 1970s.

5 Players Representing Different Social Cliques at ITP

Marc Abbey

Marc Abbey
1st Year Student
Rumor: ITP is raising money to expand floor.

David Rios

David Rios
Resident
Rumor: Foosball table needs to be packed up for thesis.

Vicci

Vicci Ho
2nd Year Student
Rumor: Dan O’Sullivan will retire next year.

Amelia

Amelia Winger-Bearskin
2nd Year Student
Rumor: Abhishek has been living on the floor for the past week.

image (3)

Shawn Van-Every
Faculty
Rumor: Shawn will move his office to Brooklyn PolyTech.

The Winner: Amelia

Screen Shot 2014-11-18 at 2.29.07 PM
“Made sure rumor was about someone that was believable – was going to first do something else but not believable enough. Spread rumor on one day only – Friday. Kelly was worried upon hearing the rumor.  Told Leslie and a few other people, but not many people (7 people total) – but told in front of other people (while lined up in front of other people). Told people in public ways because overheard gossip is more fun to spread. Abhishek played along.”

2nd Place: Vicci

vicci2
“Played actively for two days. Spread at TNO and told some people that I know would believe and tell other people. Many told friends. It was interesting.
Better strategy would be if you had more time to play the game. With 5 people spreading rumors all at once, people more likely to figure out something is happening. Stagger rumors.”

3rd Place: Marc

marc
“It was very stressful. The “space” of ITP discussion is very relevant at the moment and people reacted with emotions, making it extremely difficult to lie to people.”

Tied for Last: Shawn

Screen Shot 2014-11-18 at 9.49.33 PM
“I only used word of mouth. Students believed me but not faculty. It was hard to keep a straight face. I secretly want to move my office to Brooklyn.”

Tied for Last: Rios

Screen Shot 2014-11-18 at 2.49.59 PM
“Got too busy to tell people. Only told people on Friday. Only told a few people. So I knew I had probably lost.”

Conclusions
2nd Years and 1st Years seem isolated from each other in word of mouth communication (Vicci’s graph only shows 2nd years, and Rios (resident) only told 2nd years)

For the game to be more successful and to learn more about spread of information, needs to happen for longer period of time with more players, especially people who represent different social cliques, and rumor needs to be believable enough.

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For our final project, our team will use five Arduino Yuns. Each Arduino will receive inputs (for example, RFID) and send messages. We will create a viral infection game to expand the capabilities and reach of our network and to introduce a human element. To do this, we need to know how to use Arduino ethernet shield or Arduino Yun. The main weakness of our team across the board is physical computing, and nobody in our team has used an Arduino Yun or ethernet shield before, so we decided to focus on figuring out these boards this week.

We tried the ethernet shield first. But we realized that having to connect the ethernet shields to internet via ethernet cable is a limiting capability of the shield given ITP has ethernet connections sporadically scattered across the floor.

We decided to use Arduino Yun and use node.js to set up a simple server because communication between node.js and Arduino Yun is the best (as far as we know). We didn’t realize it would be so time consuming setting up the Arduino Yun, and spent most of the week figuring out how to get one Arduino Yun board set up.

Each Arduino Yun will be a node in our network. Each Arduino Yun has it’s own IP address and can be connected to Wifi. We don’t need an ethernet connection and only power is necessary to run this system. Since we have not tried to use Arduino Yun, we decided to set up one Arduino Yun first.

Process for setting up the board:

  1. We assigned one single IP address for one Arduino Yun. Since Arduino Yun requires Wifi connection, we also signed into ITP’s Wifi network
  2. Every Arduino Yun requires an Arduino software update (http://blog.arduino.cc/2014/04/23/upgrading-the-openwrt-yun-image-on-the-yun/)
  3. Since Arduino Yun does not have enough memory to run Node.js, we assigned Micro USB as extended memory for Arduino Yun. (http://blog.arduino.cc/2014/05/06/time-to-expand-your-yun-disk-space-and-install-node-js/)
  4. In order to install Node.js, at least 1MB internal Flash Memory in Arduino Yun is required. However, even if we pushed the format button, we could not have 1MB. So we did a factory reset and we went back to step 2.
  5. Finally, we installed Node.js in Arduino Yun. (http://www.tigoe.com/pcomp/code/arduinowiring/1216/#more-1216)
  6. We put the basic node.js code on to our Mirco USB.
  7. We used YunSerialTerminal example code to pull that js code.
  8. We ran node.js server code in Serial Terminal in Arduino program
  9. We setup a simple node.js server

We now have one Arduino Yun setup and need to get the rest of our Arduino Yuns also set up.

 

-Jiwon, Michelle, John and Haylee

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For our final project, our team will explore viral replication in our CitiBike system. For Assignment 6, Jiwon and I altered the RFID Processing sketch for two “stations,” so that one station would “infect” a vehicle that is dropped off and picked up there, and another station would “cure” our infected hosts. (Code uploaded to Google Drive!) We altered the code so that a different image and sound is triggered depending on which station is swiped with the RFID tag. Video of the assignment here.

For our final project, we are interested in having “carriers” of the virus which infect stations, but also possibly having stations that can provide cure/vaccine for the virus.

RFID Assignment 6 RFID Assignment 6

 

 

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We used network graph theory to group CitiBike stations into communities. We compared our resulting CitiBikeHoods to public transit availability, location of commercial and residential zones in NYC, and the most recent data on bike availability. Some possible findings:

  • CitiBike users who live in Lower East Side use CitiBike to commute to Lower Manhattan/Financial District
  • Midtown community users who live in residential areas of West Midtown use CitiBike to commute East to offices
  • CitiBike users who are not near public transit (Far East/West side of Manhattan) use CitiBike to compensate

citibikehoodsmap

Read more on Michelle’s blog.

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By Michelle Chandra and Jiwon Yoon

Expand the provided template model in Vensim (or develop a new one) and discuss the following questions:

What factors limit repositioning rate?
If there are too many stations, some are over used while others are under utilized.

Examples:

5 Stations, 22 vehices, 9 docks – not enough stations
Bikes get stuck in one area and visit the same three stations.
NetLogo - Too Few Stations

10 Stations, 22 vehicles, 9 docks – just the right amount of parking
-Seems to be more balanced
Balanced!

20 Stations, 22 vehicles, 9 docks (too much available parking)
Few stations are used, many stations appear to not be used (too many stations per vehicle)

Too Many Stations

The best balance is approximately half the # of stations for number of vehicles.

What is the maximum service rate?
Not sure how to calculate rate with NetLogo.

What affects unbalancing?

  • Too few stations for number of vehicles
  • Too many stations for number of vehicles
  • Number of dock s doesn’t seem so important

Kept getting following error while running simulation:
Error

What would be the optimal configuration of fleet size, parking size, and trucks?
If there are 100 bikes, there should be 50 stations. Parking doesn’t seem to matter as much as there needs to be enough stations for the number of vehicles. Not sure about trucks. Example doesn’t have trucks.

What solutions might improve the situation and what problems would they bring? 
If there are too few stations for vehicles, bikes get stuck in same stations and other stations don’t have bikes and aren’t used. If there are too many stations, still the same problem where adjacent stations aren’t used and otehr stations are over used.

Too Few Stations

Need enough stations that are the right amount of distance from each other. So if have too few stations, add more. If have too many stations, remove stations that are adjacent to overused stations.

 

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For this week’s assignment, I created a map of the movement of an individual bike from station to station in the CitiBike System using a small sample of data from May 2014. I created the map using D3, Leaflet and Stamen map tiles. Read more on my blog.

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Link to Blog: http://www.michellechandra.com/category/cloudcommuting/

After reading “Reinventing the Automobile,” I am not convinced shared driverless vehicles is an ideal future transportation system.

1) Chapter 4 describes a perfect world scenario of a shared driverless mobility system, without digging into the actual technological challenges of implementing and maintaining such a system. What would it take to implement such a system in say, NYC, and what would be the benefit to the average citizen over taking the subway or walking to a destination? What would be the cost of maintaining such a system, and how well would a networked system of sensors work in a chaotic and fragmented urban environment? In a world where getting an internet connection can oftentimes be difficult, I wonder at the feasibility of implementing a driverless mobility system reliant on networked infrastructure.

2) I disagree with the emphasis on personal autonomy over shared public transportation options. Public transportation systems such as the subway or buses provide much of the same touted benefits as driverless vehicles including time the passenger can spend consuming media, efficiency, and the opportunity to engage and interact with fellow passengers. The experience of living in large cities today can feel isolating and anonymous, and a driverless mobility system would only further increase isolation.

3) I would much rather see a discussion over eliminating the need for cars altogether, increasing public spaces and services for residents so most everything needed is within walking distance, improving underground public transportation options, and returning city streets and sidewalks to urban residents for play and interaction.

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I have tried a few different MOD sharing options including ZipCar and Uber. Perhaps the biggest issue with any one service is fragmentation. Since these services are private options, users are reliant on familiarizing themselves with the company and signing up in order to receive services. Their scope and reach is more limited, and their public visibility more hidden.

When I needed a cab ride from Brooklyn to Manhattan, I found Uber the best option because hailing a surface taxi in Brooklyn is not an option in many neighborhoods. But while living in Manhattan, I found hailing a cab quick and easy, and had no need for Uber’s services. The immediacy of being able to step outside to hail a cab within minutes outweighed loading up the Uber app and waiting for a ride to *hopefully* appear. Being in charge of one’s personal mobility by stepping outside to hail a cab *feels* more immediate and gratifying then relying on an app and a distant promise of a ride.

ZipCar is a less viable spontaneous option, but is good if one can plan ahead. It is hampered by where the vehicles are parked (proximity to user) and availability of vehicles. ZipCar pricing structure makes short trips economical but longer trips not economically viable. Living in NYC, I would love to have access to a vehicle in order to visit locations outside of the city that are difficult to reach by train. However, the cost of using a ZipCar for half a day to a day outweighs the benefit (as an individual user). This might be alleviated if ZipCar had vehicles available at train stations to allow users to travel to a train destination and get to the rest of their destination by vehicle, to make up for where public transportation leaves off. However, I have no need to use a ZipCar for short term trips because parking in Manhattan is incredibly difficult. So although it would be great to use for grocery trips, it is not a viable option in Manhattan. However, I think some of these issues are dependent on the city. When I lived in SF with friends, we often shared a ZipCar for grocery trips (parking is easier in SF!) and the cost was also distributed between us (higher financial penalty on the individual user.)

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