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He received the Ph.D. degree in Computing and Mathematics and the B.Sc. degree (1st Class Hons.) in Computer Science from the University of Bradford, United Kingdom, in 2010 and 2006, respectively. His expertise is on networking and his main research interests include intelligent networking technologies, network slicing and softwarization, future Internet architecture and technologies, Green networking, wireless networks, network security and privacy, and analytical modelling and performance optimization. He is an Editor of IEEE Transactions on Network and Service Management, Elsevier Computer Networks and IEEE Access. He contributes to major conferences on networking as various roles including a Steering Committee Chair, a General Chair, a Program Chair, and a Technical Program Committee Member.

Data is the input for various artificial intelligence (AI) algorithms to mine valuable features, yet data in Internet is scattered everywhere and controlled by different stakeholders who cannot believe in each other, and usage of the data in complex cyberspace is difficult to authorize or to validate. As a result, it is very difficult to enable data sharing in cyberspace for the real big data, as well as a real powerful AI. In this paper, we propose the SecNet, an architecture that can enable secure data storing, computing, and sharing in the large-scale Internet environment, aiming at a more secure cyberspace with real big data and thus enhanced AI with plenty of data source, by integrating three key components: 1) blockchain-based data sharing with ownership guarantee, which enables trusted data sharing in the large-scale environment to form real big data; 2) AI-based secure computing platform to produce more intelligent security rules, which helps to construct a more trusted cyberspace; 3) trusted value-exchange mechanism for purchasing security service, providing a way for participants to gain economic rewards when giving out their data or service, which promotes the data sharing and thus achieves better performance of AI. Moreover, we discuss the typical use scenario of SecNet as well as its potentially alternative way to deploy, as well as analyze its effectiveness from the aspect of network security and economic revenue. INDEX TERMS Data security, data systems, artificial intelligence, cyberspace.

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The Internet of Things (IoT) increases the number of connected devices and supports ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance computation capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels and imperfect estimation of channel state information (CSI). In this paper, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter β without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

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