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WiNEST in Madison, WI

WiNEST provides new and unique opportunities for experimentation across client devices, edge devices and in-network compute resources in a large, city-wide geographical area typical to a mobile network operator or wireless ISP. In cooperation with our industry partner, 5Nines Data, we expect to cover an area as large as 60 square miles within the City of Madison, as well as some nearby suburban and semi-rural areas.

WiNEST Architecture


Location

WiNest is focused on building infrastructure to provide computing resources in the Madison area. These compute resources are highly flexible and programmable for supporting research in many disciplines.

Wireless Resources

To provide high speed and low latency to each of the computing resources, we are deploying LTE to South Park St. The map below identifies potential locations identified where power and network connectivity are present in the city.


People

  • Faculty
    • Dr. Suman Banerjee
  • Staff
    • Derek Meyer
    • Lance Hartung
  • Students
    • Peng Liu
  • Partners
    • 5nines

http://research.cs.wisc.edu/wings/winest

How to access ParaDrop Resources

Here we will cover the basic idea of how to access ParaDrop resources.

  • Step 1
  • Step 2
    • Get a router claim number(s) by emailing info@paradrop.org
      • Please include your intended purpose so we may assign you a proper resource(s)!
  • Step 3
    • Claim your resource(s) by putting in the claim number at the bottom of the routers page on paradrop.org.
  • Step 4
    • Deploy a chute (ParaDrop Application) to your resource(s).
      • Public Chutes
        • Under the Chutes tab of paradrop.org,

Publications

  • EdgeEye - An Edge Service Framework for Real-time Intelligent Video Analytics
    Peng Liu, Bozhao Qi, and Suman Banerjee.
    ACM EdgeSys 2018, Munich, Germany, June 10-15, 2018. [PDF]
  • A Vehicle-based Edge Computing Platform for Transit and Human Mobility Analytics
    Bozhao Qi, Lei Kang and Suman Banerjee.
    ACM/IEEE SEC 2017, San Jose/Fremont, CA, October 12-14, 2017. [PDF]
  • A Wireless-Based Approach for Transit Analytics
    Lei Kang, Bozhao Qi and Suman Banerjee.
    ACM HotMobile 2016, St. Augustine, Florida, February 2016. [PDF]

Trellis Application

We developed a low cost Wi-Fi-based in vehicle monitoring and tracking system that can passively observe mobile devices and provide various analytics about people both within and outside a vehicle which can lead to interesting population insights at a city scale. Our system leverages WiNEST architecture and runs on a vehicle-based edge computing platform. The vehicle-based edge computing platform provides computing and storage resources which allow us to process massive amount of data in a timely manner. Trellis allows operators to collect various information and conduct spatial-temporal analytics in real time. With the help of WiNEST and ParaDrop, various relevant transit analytics, such as popular origin-destination stations, occupancy of vehicles, pedestrian activity trends, can be quickly derived on-board and sent back to transit operators without incurring high data requirements from the vehicles. What’s more, it is easy to deploy and manage such applications in multiple vehicles across a whole city using WiNEST and ParaDrop.

winest/start.1531944815.txt.gz · Last modified: 2018/07/18 15:13 by bozhao