CS 553: Designs of Internet Services

Rutgers University-Spring 2021

introduction

Information

Instructor: Desheng Zhang
Email: desheng AT cs.rutgers.edu
Office: CoRE 307
Grader: Dengpan Yuan
Email: dy209 AT scarletmail.rutgers.edu

Lectures: ASYNCHRONOUS: Class Video Update Weekly on SAKAI: Monday Midnight US Eastern Time
Office Hours: Monday, 7-8pm, US Eastern Time, Meeting Link shared in SAKAI
Textbooks: No books are required, and links for references and papers are provided.
Grading: 10% for Class Participation;
20% for Reading Summaries;
20% for Topic Participation;
50% for Team Project (10% for Proposal Report; 20% for Final Report; 20% for Presentation)

New Announcements

  • Jan 8th: The course ste is up and stay toned for the first lecture video.

    Syllabus

    This class is ideal for graduate students (or high-level undergrads) who want to learn various research topics about Design of Internet Services with their applications based on real-world systems and data. Some topics covered include:


    Wireless and Mobile Networks
  • 5G Cellular Network
  • Connected and Autonomous Cars

    • Internet of Things with applications to
  • Smart Cities
  • Intelligent Transportation Systems
  • Smart Health

    • Domain Specific Networks
  • Social Networks
  • Sensor Networks
  • Vehicular Networks

    • Web Srvices and Economics with applications to
  • Sharing Economy
  • Gig Economy

    • Privacy and Security of Various Networks

    Schedule Details

    Week
    Date
    Topics and Reading Assignments
    1
    Jan 25
      General Class Introduction

    2
    Feb 1
      Summary Writing and Presentation Introduction

      Invited Talks

    3
    Feb 8
      Topic 1. E-Commerce

    Reading:
    • 1. BikeCAP: Bike App Demand Prediction
    • 2. Alleviating Users' Pain of Waiting: Effective Task Grouping for Online-to-Offline Food Delivery Services
    • 3. Delivery Scope: A New Way of Restaurant Retrieval for On-demand Food Delivery Service
    • 4. Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery
    • 5. Applying Deep Learning To Airbnb Search
    • 6. ALWAES: Automatic Location-Aware Correction System for Online Delivery Platforms
    • 7. Doing in One Go: Delivery Time Inference Based on Couriers' Trajectories
    4
    Feb 15
      Topic 2. Social Networks

    Reading:
    • 1. Human mobility, social ties, and link prediction
    • 2. Friendship and mobility: user movement in location-based social networks
    • 3. Estimating Properties of Social Networks via Random Walk considering Private Nodes
    • 4. Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands
    • 5. SimClusters: Community-based representations for heterogeneous recommendations at twitter
    • 6. TIES: Temporal Interaction Embeddings for Enhancing Social Media Integrity at Facebook
      Invited Talks

    5
    Feb 22
      Topic 3. Internet of Mobile Things: Cellphone, Bikes, Tax, Buses, Scooter, Subway

    Reading:
    • 1. MultiCell: Urban Population Modeling Based on Multiple Cellphone Networks
    • 2. Mobility Modeling and Prediction in Bike-Sharing Systems
    • 3. BuScope: Fusing Individual & Aggregated Mobility Behavior for "Live" Smart City Services
    • 4. Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration
    • 5. Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records
    • 6. City Metro Network Expansion with Reinforcement Learning
    6
    Mar 1
      Topic 4. Internet of Vehicular Things

    Reading:
    • 1. BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving
    • 2. AVR: Augmented Vehicular Reality
    • 3. RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets
    • 4. Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks
    • 5. FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets
    • 6. sharedCharging: Data-Driven Shared Charging for Large-Scale Heterogeneous Electric Vehicle Fleets
    7
    Mar 8
      Topic 5. Internet of Cellular Things
    Reading:
    • 1. CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks
    • 2. MultiCell: Urban Population Modeling Based on Multiple Cellphone Networks
    • 3. AllCell:Cellular Infrastructure Sharing among All Operators of a City by Data-Driven Emulation
    • 4. Understanding Operational 5G: A First Measurement Study on Its Coverage, Performance and Energy Consumption
    • 5. CellPred: A Behavior-aware Scheme for Cellular Data Usage Prediction
    • 6. Towards Identifying Impacted Users in Cellular Services
    Spring Break
    8
    Mar 22
      Project Proposal Presentation

    No Reading Assignment
    9
    Mar 29
      Topic 6.Internet of Things: Sensing User Behaviors

    Reading:
    • 1. TransLoc: transparent indoor localization with uncertain human participation for instant delivery
    • 2. CellTrans: Private Car or Public Transportation? Infer Users' Main Transportation Modes at Urban Scale with Cellular Data
    • 3. Understanding User Behavior in Car Sharing Services Through The Lens of Mobility: Mixing Qualitative and Quantitative Studies
    • 4. Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories
    • 5. Encounter:P2Loc
    • 6. Route Prediction for Instant Delivery
    10
    Apr 5
      Topic 7.Internet of Things: Sensing Time and Locations

    Reading:
    • 1. coMobile: real-time human mobility modeling at urban scale using multi-view learning
    • 2. coSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets
    • 3. The Role of Urban Mobility in Retail Business Survival
    • 4. MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems
    • 5. EXIMIUS: A Measurement Framework for Explicit and Implicit Urban Traffic Sensing
    • 6. SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems
    11
    Apr 12
      Topic 8. Internet of Things: Sensing Physical Phenomena: Noise, Region Function, Gas Consumption, Air Quality, Fire Risk, Parking

    Reading:
    • 1. Discovering regions of different functions in a city using human mobility and POIs
    • 2. Inferring gas consumption and pollution emission of vehicles throughout a city
    • 3. U-Air: when urban air quality inference meets big data
    • 4. CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach
    • 5. Hard to Park?: Estimating Parking Difficulty at Scale
    • 6. Diagnosing New York city's noises with ubiquitous data
    12
    Apr 19
      Topic 9. Privacy & Security

    Reading:
    • 1. Why Are They Collecting My Data?: Inferring the Purposes of Network Traffic in Mobile Apps
    • 2. Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
    • 3. PrivateBus: Privacy Identification and Protection in Large-Scale Bus WiFi Systems
    • 4. Elastic pathing: your speed is enough to track you
    • 5. Anonymization of location data does not work: a large-scale measurement study
    • 6. A Taxi Driving Fraud Detection System
    13
    Apr 26
      Topic 10. Invited Lectures

      Three Invited Lectures

    14
    May 3
      Final Project Presentation

    No Reading Assignment
    May 10
    		   Final project papers are due on May 10th 11:59PM EST.


    "How to" List


    1. How to Read a Paper by S. Keshav.
    2. How to Read a Research Paper by Michael Mitzenmacher.
    3. Writing Reviews for Systems Conferences by Timothy Roscoe.
    4. How to Read an Engineering Research Paper by William Griswold.
    5. How to Read a Research Paper by Spencer Rugaber.
    6. How to write a great research paper by Simon Peyton Jones.
    7. How to give a great research talk by Simon Peyton Jones.