Emerging on-demand services, such as Uber and GoGoVan in Hong Kong, provide a platform for users to request services on the spot and for suppliers to meet such demand. Uber, for example, allows its users to call for taxies on demand through mobile apps. The demand is dispatched by Uber to the drivers, who decide whether or not to accept them. If multiple drivers are willing to serve, the order will be assigned to one of them according to pre-established policies. Future energy systems can also be considered as on-demand services, as multiple suppliers, including power stations, energy stores, and mobile charging stations, compete to satisfy energy demand from their customers. This project aims to study the fundamental problem of demand dispatching in these emerging on-demand services, with the objective of maximizing supplier profits and saving user costs.
Detect and monitor human's information using wireless technologies in a non-intrusive manner.
This project aims to sense human activities with wireless signals in a non-intrusive way. We now focus on solving problems on multi-person wireless sensing, including multi-person respiration monitoring and people counting using WiFi devices.
In this project, we build a physical model for counting people passing by the gateway based on the phase difference information of wireless signals. We propose to realize people counting in a low-cost (using WiFi-existing infrastructure), accurate (can count multiple people passing by at the same time) and non-intrusive (no need for carring devices and active participation, privacy-protected).
We aim to develop distributed algorithms and learning algorithms for better controlling the multi-robot systems. Our long-term goals are to understand the computation of multi-robot systems in both theory and practice.
An Ensemble-Level Programming Model with Real-Time Support for Multi-Robot Systems.