This shows you the differences between two versions of the page.
Both sides previous revision Previous revision | |||
winest:start [2018/07/22 17:00] suman [How to access ParaDrop Resources] |
winest:start [2018/07/22 17:17] (current) suman [An Example Application: Trellis] |
||
---|---|---|---|
Line 84: | Line 84: | ||
---- | ---- | ||
- | ===== An Example Application: Trellis ===== | + | ===== Anatomy of an Application: Trellis ===== |
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. | 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. | 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. |