CSE 40437/60437 - Social Sensing and Cyber-Physical Systems - Spring 2017


  • Prof. Dong Wang
    dwang5 at nd dot edu
    Office Hours: Mon 3:15-5:15 PM, 214B Cushing Hall,
  • TA: Daniel Zhang
    yzhang40 at nd dot edu
    Office Hours: Mon 10 am-noon, Wed 4-6 PM, Engineering Library (1st floor of Fitzpatrick)
  • TA: Steven Mike
    smike at nd dot edu
    Office Hours: Tue 2:30-4:30 PM, Thu 2:30-4:30 PM, Engineering Library (1st floor of Fitzpatrick)
  • Lecture Time

    Monday and Wednesday, 2:00-3:15 pm, DeBartolo Hall 125

    Course Overview

    Online social media (e.g., Twitter, Facebook), smartphones, and ubiquitous internet connectivity have greatly facilitated data sharing at scale, allowing for a firehose of human and sensor observations to pour in about the physical world in real-time. This opens up unprecedented challenges and opportunities in the field of social sensing and cyber-physical systems (CPS) where an important goal is to efficiently organize the real-time data feeds and accurately reconstruct the "states of the world", both physical and social. This course offers students the opportunity to learn the theoretical foundations, state-of-the-art techniques, and hands-on experience in this exciting area. The topic of this class is timely due to the increasing interest in online social networks, big data, and human-in-the-loop systems, as well as the proliferation of computing artifacts that interact with or monitor the physical world.

    The class contains four main components: (i) the introduction to social sensing and cyber-physical systems; (ii) key technical challenges (e.g., big data analytics, system reliability, user mobility, energy, privacy, etc.); (iii) state-of-the-art techniques and systems (e.g., MapReduce/Hadoop, fact-finding, etc); (iv) emerging applications (smartphone-based crowdsensing, online social media sensing, participatory/opportunistic sensing, intelligent transportation, smart buildings, body area networks etc). The students will have the opportunities to work with real world social sensing and cyber-physical system problems.

    Getting Help

  • Piazza Discussion Page - General announcement and Q&A after class
  • Office Hours - Please refer to the above schedule.
  • Email - Contact Prof. Wang for questions about grades, course policies, etc.
  • Grades are available in Sakai.
  • Course Documents

  • Syllabus
  • Course Project
  • Assignment 1: Build your own Twitter data crawler
  • Assignment 2: Tweets Clustering
  • Assignment 3: Truth Discovery and Credibility Analysis for Twitter
  • Mid-term Project Presentation
  • In-class Paper Presentation
  • Final Project Presentation

  • Project Meeting Doodle Links

  • Project Kick-off Meetings
  • Project Mid-term Meetings
  • Project Pre-Final Meetings
  • Grading

  • 10% of the grade will be assigned on individuals' class participation and proactive discussion of lecture topics and project presentations (Individual based).

  • 10% of the grade will be assigned on an in-class paper presentation on the selected topic by each group. (Group based)

  • 30% of the grade will be assigned on individuals' homework assignments (Individual based).

  • 50% of the grade will be determined by a group course project. This grade includes project proposal, mid-term report, mid-term project presentation, a final project presentation, a final project paper, and project updates and demonstrations (to the instructor). The project will implement some innovative social sensing model, service, system, or computing environment. Students will be allowed to work in groups of 2 or 3 on the project. The project will proceed through the landmarks stated below. (Group based)

  • 5%: Project discussion and updates

  • 5%: Project proposal

  • 5%: Mid-term project presentation

  • 10%: Mid-term project report

  • 10%: Final project presentation

  • 15%: Final project paper

  • Note: For individual based work, each student will receive the credit based on her/his own work. For the group based work, every student in the group will receive the same credit based on the group's work.
  • This class follows the binding Code of Honor at Notre Dame. The graded work you do in this class must be your own. In the case where you collaborate with other students make sure to fairly attribute their contribution to your project. You must read and abide by the Academic Code of Honor. http://honorcode.nd.edu

    Course Project

  • The project will be chosen by each group within the first couple of weeks of class. Here are some ideas to help you get started. Groups are encouraged to come up with their own ideas. If you have some really cool idea that does not satisfy such restriction, please schedule a meeting to discuss it with the instructor. Project title, abstract, and member list are due on Noon, February 3.

  • Each group will schedule a regular meeting (during project meeting slots and office hours) to meet with the instructor and discuss the progress and problems encountered on their projects.

  • Each group will prepare and submit a two page project proposal. The proposal should include an overview of the project (preferably with a diagram), a brief review of state-of-the-arts in related fields, a credible set of initial project results if available, a list of further proposed milestones, and a plan of action for the rest of the semester. The proposal is due on Noon, February 24.

  • Each group is responsible for a Mid-term Project Presentation in class starting from March 20. The presentation will allow the instructor and classmates to comment on the initial results and current state of the project and also give constructive feedback to the group members.

  • Each group will prepare and submit a four page mid-term project report. The mid-term report should include a reasonable amount of preliminary results, a description of finished milestones, a discussion of encountered problems and relevant solutions, and any modifications to the plan (if there are) to finish the remaining tasks. The mid-term report is due on Noon, March 24.

  • Final project presentations will be conducted by each group in the week of May 1

  • Each group will prepare and submit a final project paper. The final project paper is a comprehensive summary of the whole project and should follow a technical paper writing style. The expected number of pages for the final paper is 8 to 10 pages (including references). Final project paper is due on Noon, May 8.

  • The proposal, mid-term report and final project paper should all follow a standard technical paper format . Here is the template: IEEE Latex or Word Template .

  • A successful project could result in a conference or journal quality paper.

  • Note : For more information about the project (e.g., possible ideas and milestones), please visit Course Project Page

    You are encouraged to seek out and exploit external manuals, books, websites, and other documentation that can help you to complete your project, provided that you indicate what sources you have used. However, all software development, experimental work, and writing of the proposal, report and paper must be done solely by you and your project partner(s).

    Project Submit Instruction: To submit the project related documents (i.e., abstract, proposal, midterm/final presentation slides and reports), please (1) email the document (i.e., pdf for text documents and pptx/pdf for slides) directly to Prof. Wang before the deadline; (2) upload a copy of the submission to dropbox folder as a backup. One submission per group is sufficient.


    Assignments are normally due at the beginning of class on the date due . This might change due to the break (e.g., Spring Break). Please double check with the assignment description and the course website for the actual due date. Late assignments will receive no credit. This includes assignments submitted after class has begun.

    Programming assignments will be turned in electronically by copying all required files to a "dropbox" directory. You are free to turn in assignments multiple times before the deadline expires. It would be a good habit to turn in an incomplete but working assignment on a daily basis. Thus, there is no excuse for failing to turn in an assignment: everyone should turn in something long before the deadline. Exceptions will be made only in grave circumstances.

  • Assignment 1: Build your own Twitter data crawler
  • Assignment 2: Tweets Clustering
  • Assignment 3: Truth Discovery and Credibility Analysis for Twitter

  • In-class Paper Presentation

  • Each group will do an in-class paper presentation to present a selected technical paper in late April.

  • The in-class paper presentation will provide good opportunities for you to exercise your scientific presentation ability, practice critical thinking, understand how to judge and challenge other's work in a professional way, and learn how to organize and lead an active scientific/technical discussion session.

  • Detailed instructions are available here
  • Tentative Schedule

    Note: Lecture notes are avaliable on Sakai.
    Week Lecture Materials
    Jan 16 - 23 Social Sensing and Cyber-Physical Systems Landscape Reading:
    Introduction to Social Sensing
    Cyber-Physical Systems: The Next Computing Revolution
    Assignment 1 is out, due: Feb. 6
    Jan 23 - 30 Classical CPS Challenges Reading:
    Liu and Layland Seminal Paper
    Priority Inheritance Protocols
    A Comprehensive Suervey on Real-time Scheduling Theory
    Jan 30 - Feb 6 Data Reliability and Information Overload Reading:
    Truth Discovery in Social Sensing
    Quantifying the Quality of Information
    Using Humans as Sensors
    Project Title, Abstract and Member List Due Friday, Feb. 3
    Feb 6 - 13 Project Kick-off Meetings Please sign up your meeting slot on Doodle
    Assignment 2 is out, due: Feb. 20
    Feb 13 - 20 Data Reliability and Information Overload Cont. Reading:
    Exploitation of Physical Constraints
    Handling Conflicting Claims
    Provenance-Assisted Social Signal Classification
    Feb 20 Online Social Media Sensing Reading:
    Earthquake Shakes Twitter Users
    From Tweets to Polls
    You Are Where You Tweet
    Groundhog Day: Near-Duplicate Detection on Twitter
    Project Proposal Due Friday, Noon, February 24
    Feb 27 Project Mid-term Meetings Please sign up your meeting slot on Doodle
    Assignment 3 is out, due: March 8
    March 6 Big Data Issues Reading:
    Big Table Paper
    Map-Reduce Paper
    Data Cube Paper
    March 13 Spring Break
    March 20 Mid-term Project Presentations Mid-term Project Presentation
    Project Mid-term Report Due Friday, Noon, March 24
    March 27 Crowdsensing and Mobile Sensing Reading:
    A Survey of Mobile Sensing
    How Long to Wait: Bus Arrival Time Prediction
    Automatically Characterizing Places
    April 3 Automotive Sensing and Intelligent Tranportation Reading:
    GreenGPS: A Participatory Sensing Fuel-Efficient Maps Application
    SignalGuru: A Collaborative Traffic Signal Schedule Advisory Service
    CarSpeak: A Content-Centric Network for Autonomous Driving
    April 10 Body-Area Sensor Network and Pre-Final Project Meetings Reading:
    Body Sensor Networks (BSN)
    BodyScope: A Wearable Acoustic Sensor for Activity Recognition
    Please sign up your meeting slot on Doodle
    April 17 Guest Lecture on April 19 April 17 (Mon): Easter Holiday
    Guest Lecture (April 19, Wed):
    Principled Structure Discovery from Networks , Prof. Tim Weninger
    April 24 Medical Sensing, Privacy or Open Issues
    (Students In-class Presentation)
    In-class Paper Presentation
    Medical Sensing:
    Detecting Cocaine Use with Wearable Electrocardiogram Sensors
    Sensor Selection for Energy-Efficient Ambulatory Medical Monitoring
    Real-time Clinical Monitoring and Deterioration Warning
    Context-Aware Assisted-Living and Residential Monitoring
    Cyber-Physical Modeling of Implantable Cardiac Medical Devices
    BiliCam: Using Mobile Phones to Monitor Newborn Jaundice
    Contactless Sleep Apnea Detection on Smartphones
    ProtectMyPrivacy: Detecting and Mitigating Privacy Leaks on iOS Devices
    Cloud-Enabled Privacy-Preserving Collaborative Learning for Mobile Sensing
    Understanding Users' Mental Models of Mobile App Privacy through Crowdsourcing
    Privacy Manipulation and Acclimation in a Location Sharing Application
    Privacy-aware Regression Modeling of Participatory Sensing Data
    Privacy.Tag: Privacy Concern Expressed and Respected
    Privacy-Preserving Compressive Sensing for Crowdsensing based Trajectory Recovery
    May 1 Final Project Presentations Final Project Presentation
    Please sign up your final project presentation slot on Doodle
    Final Project Paper Due Monday, Noon, May 8