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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 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. With fixed resources located around State Street and the capitol areas, there are opportunities experimentation in a real world scenario. Further deployments currently underway are wireless resources and compute resources along Park Street.
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.
We intend to have wireless resources in research vehicles along this route to test various driving scenarios. With ParaDrop in the Innova Urban Electric Vehicles, we will have hotspots for the occupants to be able to test applications in mobile environments with local computing resources located inside the vehicle.
http://research.cs.wisc.edu/wings/winest
Here we will cover the basic idea of how to access ParaDrop resources.
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.