IEEE International Workshop on Knowledge Centric Networking (KCN 2018)
Monday, 16 April, 2018 ● 08:00 – 12:00 ● Room: Iolani 2
Machine and deep learning become increasingly popular and achieve remarkable success nowadays in many application domains, e.g., speech recognition, bioinformatics and computer vision. Machine learning is capable to exploit the hidden relationship from voluminous input data to complicated system outputs, especially for some advanced techniques, like the deep learning. Moreover, some other techniques, e.g., reinforcement learning, could further adapt the learning results in the new environments to evolve automatically. These features perfectly match the complex, dynamic and time-varying nature of today’s networking systems. This workshop focus on discussing a new networking paradigm — knowledge centric networking (KCN). The key insight is applying the emerging machine or deep learning techniques and leveraging the big data to derive remarkable knowledge for benefiting the system design, instead of viewing them as undesired burdens. On the other hand, benefiting from the IoT, big data is mature already for an immediate usage in various networking systems. Thus, we can envision KCN as a rewarding solution to leverage learning techniques on the pervasively available data contents to create knowledge and facilitate the networking system designs.
This workshop presents the state-of-the-art research in KCN, including both theoretical and system aspects to encompass novel contributions in the field of learning techniques applied to communication systems and networks.
General Chairs:
Baochu Li (University of Toronto, Canada)
Dapeng Oliver Wu (University of Florida, USA)
Technical Program Chairs:
Yong Li (Tsinghua University, China)
Zhenjiang Li (City University of Hong Kong, Hong Kong)
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08:00–10:00
Session 1: Knowledge creation & composition
A Repeated Stochastic Game Approach for Strategic Network Selection in Heterogeneous Networks
Xin Li, Qiuyuan Huang, Dapeng Oliver Wu (University of Florida, USA)
A Unified Clustering Approach for Identifying Functional Zones in Suburban and Urban Areas
Jingyuan Yang (Rutgers University, USA), Jin Cao (Nokia Bell Labs, USA), Ran He (Bell Labs, USA), Lisa Zhang (Nokia Bell Labs, USA)
Course Recommendation of MOOC with Big Data Support: A Contextual Online Learning Approach
Yifan Hou, Pan Zhou (Huazhong University of Science and Technology, China), Jie Xu (University of Miami, USA), Dapeng Oliver Wu (University of Florida, USA)
Buildings' Producing Filter Effect on PM2.5 Data: A Model-Fitting Approach
Haina Zheng, Ke Xiong (Beijing Jiaotong University, China), Pingyi Fan (Tsinghua University, China), Zhangdui Zhong (Beijing Jiaotong University, China)
11:00 – 12:00
Session 2: Knowledge distribution
Knowledge-centric Proactive Edge Caching Over Mobile Content Distribution Network
Hao Hao, Changqiao Xu (Beijing University of Posts and Telecommunications, China), Mu Wang (State Key Laboratory of Networking and Switching Technology, China), Haiyong Xie, Yifeng Liu (China Academy of Electronics and Information Technology, China), Dapeng Oliver Wu (University of Florida, USA)
An SDN Framework for UAV backbone Network towards Knowledge Centric Networking
Xiao Zhang, Haijun Wang, Haitao Zhao (National University of Defense Technology, China)