The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional WSNs. This presentation first discusses the characteristics and challenges of the management of big sensory data. It then reviews some recent efforts by the speaker and his research group at Georgia State University, including 1) approximate data acquisition for physical world reconstruction, 2) kernel dataset extraction from big sensory data, and 3) integration model construction for multimodal sensory data.

Zhipeng Cai received his PhD and MS degrees from the Department of Computing Science at University of Alberta, and BS degree from the Department of Computer Science and Engineering at Beijing Institute of Technology. He is currently an Assistant Professor in the Department of Computer Science at Georgia State University. Dr. Cai's research areas focus on Networking and Big data. Dr. Cai is the recipient of an NSF CAREER Award. He actively participates in professional activities including several editor positions and chair positions for many prestigious journals and conferences, including associate editor for International Journal of Sensor Networks, guest editors for Algorithmca, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Theoretical Computer Science, and program chairs for COCOON 2014, WASA 2014, IPCCC 2013, and ISBRA 2013.