1. Leverage the data characteristics of applications and computing to reduce the communication cost in WSNs. Design advanced algorithms and mechanisms to improve the communication system performances. Research dimension

2. Application of compressive sensing in radio map construction A good radio map with fine resolution is valuable for – network planning and optimization such as anticipated networking – RSSI fingerprinting for indoor localization – Etc. However, it takes time and resource to construct it. How to apply compressive sensing to effectively construct such a radio map with fewer measurements – Estimate the required number of samples – where to sample – Require a systematic approach considering communication protocol design Implement this approach in smartphone or sensor motes Qi Zhang qz@eng.au.dk E314

3. Application of Compressive sensing in wind monitoring Valuable in renewable energy prediction, smart grid, weather forecast etc. Apply compressive sensing – To reduce the amount of data collection considering temporal or spatial correlation independently – How to achieve better spare reconstruction leveraging joint temporal and spatial correlation – How to extend CS theory in the vector fields – How to integrate CS with communication protocol design Qi Zhang qz@eng.au.dk E314