IEEE SIG on Green Cognitive Communications and Computing Networks

Name of SIG: Green Cognitive Communications and Computing Networks

This SIG were with the history from July 2012 to May 2015. The leadership team have retired now.This SIG have been now replaced by a new SIG 

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Chair: Ekram Hossain, Dept. of ECE, University of Manitoba, Canada
Email:
Ekram.Hossain@ad.umanitoba.ca
 
Vice-Chair: David Grace, The University of York, UK
 Email:
dg@ohm.york.ac.uk

Vice-Chair: Liqun Fu, The Chinese University of Hong Kong
 Email:
lqfu@inc.cuhk.edu.hk

Advisor: Ying Chang Liang, Institute for Infocomm Research (I2R), A*STAR, Singapore
Email:
ycliang@i2r.a-star.edu.sg
 
LinkedIn discussion site:

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Scope and Objectives:
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The SIG will focus on improving the energy efficiency of cognitive communications and computing networks. Cognitive network, which allows secondary users to opportunistically access the network that is under-utilized by the primary users, is a promising approach to improve the transmission efficiency of the communications and computing networks. However, higher spectrum efficiency is usually achieved at the expense of higher energy consumption. In recent years, energy and power efficient designs of communication networks have become more crucial because of the steadily rising energy cost and environmental concerns.  Thus, there is an urgent need to address the energy efficiency in cognitive networks, which will be the focus of this SIG. This SIG will mostly concentrate on the energy efficient design for cognitive communications and computing networks. The goal is to establish a foundation for energy-efficient cognitive communications and computing network design with multiple objectives, such as high energy and spectrum efficiency, fairness, simplicity, robust convergence, and scalability.

Different from other types of networks, cognitive network has its own features. For instance, spectrum sensing is a critical component in cognitive radio networks. In order to obtain reliable channel detection, a significant amount of energy is spent at the spectrum sensing. Thus, energy-efficient spectrum discovery techniques are of great importance for the energy efficient design of cognitive radio networks.

The cognitive networks usually adopt a heterogeneous network structure, as there are usually two or more service providers. The collaboration and the cooperation techniques in network sharing are critical in order to reduce the energy consumption. Furthermore, the resource allocation and the cross-layer design approach are desirable for improving the energy efficiency of cognitive networks.

This SIG will look into the ongoing standardization activities, such as LTE/LTE-A networks, and promote to adopt cognitive and energy-efficient communications techniques in the evolving systems. Furthermore, another important component of this SIG is to improve the energy-efficiency of cognitive radio application scenarios (e.g., Wireless Regional Area Network (WRAN)/IEEE 802.22, public safety communications, etc.).

This SIG will also look into the energy efficiency of cognitive computing. Cognitive computing is closely related to cognitive networks but with different focus. Cognitive computing focus on developing computing systems modeled after the human cognitive systems and to teach computers to think and reasoning like a human mind. The architecture, algorithms, and techniques of improving the energy efficiency of cognitive computing system will therefore be a key component of this SIG.

Last but not the least, cognitive principles, not only improves the spectrum efficiency, and the energy efficiency, but also has the potential to improve other aspects of Green IT, such as electromagnetic pollution mitigation, resource and materials reusing, and creating human friendly environments. How to use the cognitive principles with a system objective of improving the non-energy issues of Green IT will be an important component of this SIG. 

Within the framework of “Green Cognitive Communications and Computing Networks”, a number of paradigm-shifting technical approaches can be expected, including but not limited to the following:
 -Network architecture design for energy efficient cognitive networks
 -Energy-efficient spectrum sensing techniques for cognitive networks.
 -Economic models and game theory for energy efficient cognitive networks
 -Energy-efficient physical (PHY) layer design of cognitive networks
 -Energy-efficient medium access control (MAC) for cognitive networks
 -Cross-layer optimization for energy efficient cognitive networks
 -Cooperative techniques for energy-efficient cognitive networks
 -Energy-efficient resource management for cognitive networks
 -Multiuser/single-user MIMO techniques for energy-efficient cognitive networks
 -Signal processing challenges for energy-efficient cognitive networks
 -Energy-efficiency evaluation and measuring techniques for cognitive networks.

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•Founding Members:
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1. Hüseyin Arslan, University of South Florida, USA,
2. Özgür B. Akan, Koc University, Turkey,
3. Wei Chen, Tsinghua University, China,
4. Peter Han Joo Chong, Nanyang Technological University, Singapore,
5. Lingjie Duan, Singapore University of Technology and Design, Singapore,
6. Liqun Fu, The Chinese University of Hong Kong,
7. David Grace, The University of York, UK
8. Ekram Hossain, Dept. of ECE, University of Manitoba, Canada
9. Fen Hou, Macau Polytechnic Institute, Macau,
10. Ying Chang Liang, Institute for Infocomm Research (I2R), A*STAR, Singapore,
11. Hongseok Kim, Sogang University, Korea,
12. Victor C. M. Leung, University of British Columbia, Canada,
13. Guowang Miao, KTH Royal Institute of Technology, Sweden,
14. Dusit Niyato, Nanyang Technological University, Singapore,
15. Jacques Palicot, SUPELEC, France,
16. Yi Qian, University of Nebraska – Lincoln, USA
17. Tapani Ristaniemi, University of Jyväskylä, Finland,
18. Jinsong Wu, Bell Laboratories, China,
19. Honggang Zhang, Zhejiang University, China,
20. Wei Zhang, The University of New South Wales, Australia,
21. Ying Jun (Angela) Zhang, The Chinese University of Hong Kong, Hong Kong
22. Mustafa Cenk Gursory, Syracuse University, USA
23. Rose Qingyang Hu, Utah State University, USA
24. Himal A. Suraweera, Singapore University of Technology and Design, Singapore
25. Jan Erik Håkegård, SINTEF ICT, Norway
26. Lin Chen, University of Paris-Sud, France
27. Wei Wang, Zhejiang University, China
28. Alagan Anpalagan, Ryerson University, Canada
29 .Athanasios V. Vasilakos, University of Western Macedonia, Greece
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