Zhe Zhang

Email: zhezhang@njupt.edu.cn

I am an assistant professor at Nanjing University of Posts and Telecommunications (NJUPT).

My research interests include Information-centric Networking (ICN), in-network caching, IoT, edge computing, data center networking (DCN). I also have a strong interest in using machine learning techniques to improve the performance of networks.

I received my Ph.D. degree from the Department of Systems and Computer Engineering, Carleton University on Sep 19, 2019. Many thanks to my supervisor Chung-Horng Lung, co-supervisor Marc St-Hilaire and Ioannis Lambadaris.

My new homepage moved to the following:

Call for workshop papers:

THE 17th INTERNATIONAL CONFERENCE ON GREEN, PERVASIVE, AND CLOUD COMPUTING, 2-4 December Chengdu, Sichuan, China

http://2022.gpc-conf.org/workshops.html

General Information :

The continuous evaluation of networking technologies has led the number of edge devices (e.g., sensors, smart phones, actuators, e.g.) to be growing at an unprecedented pace. These edge devices generate an excessive amount of data, and traditionally they have to transfer the collected data through the backbone networks to data centers for further computing, storing, and analyzing. However, the long delivery path introduces unignorable latency which significantly degrades the user experience. To reduce the data transmission latency, edge servers have been deployed at the network edge for replacing the far away data center servers. However, running those edge servers also consumes tremendous energy. In addition, since the edge devices are battery-powered in general, they also have to face the energy efficiency issue. Therefore, how to efficiently transfer the excessive amount of collected data from the edge devices to the edge servers, and reduce the energy consumption for both of them has been a crucial challenge. Compared to the traditional approaches, machine learning (ML) or artificial intelligence (AI) technologies are believed to be more promising for solving the above challenges.

Thus, GPC 2022 Workshop on Edge Intelligence for Energy Efficient Content Delivery (EIEECD) seeks to bring together researchers and experts from academia, industry, and government agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic. Suitable topics for this workshop include, but are not limited to, the following areas:

  • Wireless network optimization for improving the performance of edge computing

  • Self-adaptive energy-aware routing algorithms

  • AI-based energy efficient edge caching algorithms

  • Energy-aware efficient ML/AI algorithms for edge devices

  • Offloading and scheduling strategy for edge intelligence

  • Architecture and applications of edge intelligence for IoT

  • AI-based traffic engineering for edge networks

  • Reinforcement learning for network decision making, network control, and management

  • Multi-agent deep reinforcement learning for energy aware resource allocation

  • Federated learning for edge networks

  • Blockchain for edge intelligence

  • Energy Efficient resource allocation in edge networks


Important dates:

Deadline for submission of workshop papers: Aug. 30, 2022

Notification of acceptance of workshop papers: Sep. 6, 2022

Camera Ready Version: Sep. 13, 2022,

Authors Registration Deadline: Sep. 20, 2022


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