· Connected and Autonomous Vehicles (CAVs). Connected vehicle systems provide connectivity among vehicles to enable crash prevention, between vehicles and the infrastructure to enable safety, mobility and environmental benefits; among vehicles, infrastructure, and wireless devices to provide continuous real-time connectivity to all system users. An autonomous vehicle (driverless vehicle, self-driving vehicle, robotic vehicle) is a vehicle that is capable of sensing its environment and navigating without human input. The potential of CV and AV has been acknowledged with the establishment of ambitious research programs around the globe. Despite the potential vision of CV and AV systems, there are numerous design challenges remaining to be addressed before widespread deployment of CV and AV systems.
News reports about my research on CAVs:
· Jan. 2018, Ingenious Magazine, Connecting and Protecting Canada’s Roadways.
· Oct. 12, 2017, Carleton Newsroom, Carleton’s Richard Yu Receives Funding for Research on Connected and Autonomous Vehicles.
· Wireless Cyber-Physical Systems. Wireless cyber-physical systems (CPS) are integrations of wireless communication, computation, networking, and physical processes. Examples of wireless CPS include connected and autonomous vehicles, intelligent transportation, communication-based train control (CBTC), industrial Internet, smart grid, etc. CPS integrates the dynamics of the physical processes with those of networking, providing abstractions and modeling, design, and analysis techniques for the integrated whole. Although the layered structure is one of the key reasons behind the success of the Internet, cross-layer/cross-system design is appropriate and may even be necessary in wireless CPS.
News reports about my research on wireless systems:
· Feb. 15, 2017, CBC News, Carleton Prof Award $600,000 Grant to Improve 5G Networks.
· Feb. 15, 2017, Carleton Stories, Bringing 5G Wireless to Life – Richard Yu’s Award-Winning Research.
· Security and Privacy in Networks. Security and privacy are becoming more and more important issues in networks. Two classes of approaches, prevention-based (such as authentication) and detection-based (such as intrusion detection), can be used to protect high security wireless networks. As the front line of defense, user authentication is crucial for integrity, confidentiality and non-repudiation. Moreover, intrusion detection systems (IDSs), serving as the second wall of protection, can effectively help identify malicious activities. In addition, privacy concerns have drawn more and more attentions, involving the right of mandating personal privacy concerning storing, re-purposing, provision to third parties, and displaying of information pertaining to oneself via the network. Many technical challenges remain to be addressed to develop security and privacy schemes in future networks.
News report about my research on security:
· Aug. 19, 2014, Defence Research and Development Canada, Tactical Wearable and the Self-Organizing Network. (Search ‘Richard’ on the page)
· Machine Learning and Artificial Intelligence. Recent advances in machine learning and artificial intelligent can provide solutions to the above problems. Machine learning can be roughly classified into three categories: supervised, unsupervised and reinforcement learning. Reinforcement learning is an important branch of machine learning, where an agent learns to take actions that would yield the most reward by interacting with the environment. By replacing ordinary neural networks with advanced multi-layer deep neural networks, recently, deep reinforcement learning is proven to be more advantageous with greater performance and more robust learning.
· Y. He, F. Richard Yu, N. Zhao, V.C.M. Leung, and H. Yin, “Software-defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach,” IEEE Comm. Mag., vol. 55, no. 12, pp. 31-37, Dec. 2017.
· Y. He, Z. Zhang, F. Richard Yu, N. Zhao, H. Yin, and V.C.M. Leung, “Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks,” IEEE Trans. Veh. Tech., vol. 66, no. 11, pp. 10433 – 10445, Nov. 2017
· L. Zhu, Y. He, F. Richard Yu, B. Ning, T. Tang, and N. Zhao, “Communication-Based Train Control System Performance Optimization Using Deep Reinforcement Learning,” IEEE Trans. Veh. Tech., vol. 66, no. 12, pp. 10705-10717, Dec. 2017.