Officers：

Chair: Brij B. Gupta, Asia University, Taiwan. bbgupta AT asia.edu.tw

Co-Chair: Priyanka Chaurasia, University of Ulster, UK, p.chaurasia AT ulster.ac.uk

Co-Chair: Kwok Tai Chui, Hong Kong Metropolitan University (HKMU), Hong Kong, China

Co=Chair: Chunsheng Zhu, Southern University of Science and Technology, China, chunsheng.tom.zhu AT GMAIL.COM

Co-Chair: Francesco Colace, University of Salerno, Italy, fcolace AT unisa.it

Co-Chair: William Liu, Unitec, New Zealand, wliu AT unitec.ac.nz

Co-Chair: Wei Wei, Xi’an university of Technology, P. R. China, weiwei AT xaut.edu.cn

jktchui AT hkmu.edu.hk

Co-Chair: S. K. Singh, Indian Institute of Technology (BHU) Varanasi (IIT-BHU), India, sks.cse AT iitbhu.ac.in

Advisors:

Jinsong Wu, University of Chile, 8370451, Chile, wujs AT ieee.org

Amiya Nayak, Professor, University of Ottawa, Canada, nayak AT uottawa.ca

Francesco Palmieri, University of Selerno, Italy, fpalmieri AT unisa.it

Andrew Ip, University of Saskatchewan, Canada, mfwhip AT polyu.edu.hk

Dragan Peraković, University of Zagreb, Croatia, dperakovic AT fpz.unizg.hr

Michael Sheng, Macquarie University, Sydney, Australia, michael.sheng AT mq.edu.au

Francisco José García-Peñalvo, University of Salamanca, Spain, fgarcia AT usal.es

Founding Members:

Arcangelo Castiglione, University of Salerno, Italy

Chinthaka Premachandra, Shibaura Institute of Technology, Japan

Sugam Sharma, Iowa State University, United States

Wadee Alhalabi, Department of Computer Science, KAU, Saudi Arabia

Vipindev Adat Vasudevan, Massachusetts Institute of Technology (MIT), USA

Deepak Gupta, Founder and CEO, LoginRadius Inc., USA

Chunjia Han, Birkbeck, University of London, UK

Justin Zhang, University of North Florida, USA

Mohammed Ali, University of Manchester, UK

Gregorio Martinez Perez, University of Murcia (UMU), Spain

Zhili Zhou, Nanjing University of Information Science and Technology, NUIST, China

Sunil Kumar Singh, CCET, Panjab University, Chandigarh, India

Zhiyong Zhang, Henan University of Science and Technology, China

Ankit Kumar Jain, National Institute of Technology Kurukshetra, India

Kostas Psannis, UoM, Thessaloniki, Greece

Ajey Kumar, Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India

Xiaojun Chang, Monash University Clayton Campus, Australia

Manoj Gupta, SMVDU, Katra, Jammu and Kashmir, India

Pethuru Raj, Vice President, Reliance Jio Infocomm. Ltd (RJIL), India

Sachin Sharma, State Bank of India, India

Marjan Kuchaki Rafsanjani, Shahid Bahonar University of Kerman, Kerman, Iran

Mosiur Rahaman, Asia University, Taiwan. Email:  mosurrahaman AT  asia.edu.tw

Syed Taqi Ali, Visvesvaraya National Institute of Technology Nagpur, India

Chun-Yuan Lin, Asia University, Taichung City, Taiwan. Email: cyulin AT asia.edu.tw

Vijay Kumar, Anna University, Chennai, India

Akshat Gaurav, Ronin Institute, Montclair, USA

Domenico Santaniello, University of Salerno, Italy

Konstantinos Psannis, University of Macedonia, Greece

Mirjana Pejić Bach, University of Zagreb, Croatia

Chi-Hua Chen, Fuzhou University, China

Neeraj Kumar, Thapar University, Patiala, India

Melody Moh, San Jose State University, USA

Chang Choi, Department of Computer Engineering, Gachon University, Republic of Korea

Bambang Irawan, Esa Unggul University, Indonesia

Scope and Objective

The SIG on Green Big Data focuses on investigating the relations of green and sustainable development to big data and relevant systems. The exponential growth of data across various sectors presents unique challenges and opportunities for developing eco-friendly and energy-efficient big data solutions. This SIG will explore innovative ways to reduce the environmental impact of big data processing and storage, promote green and sustainable development via big data technologies.

A primary objective of this SIG is to pioneer energy-efficient data processing techniques. Recognizing the significant energy demands of big data operations, this goal focuses on minimizing the carbon footprint associated with such activities. Strategies may include optimizing data processing algorithms for lower energy consumption, innovating in data compression and storage methods to reduce energy usage, and developing more efficient data processing architectures.

The SIG aims to promote and develop sustainable practices in the design and operation of data centers. This involves the integration of renewable energy sources, the implementation of advanced cooling systems to reduce energy consumption, and the exploration of innovative building designs that minimize environmental impact. The goal is to create a blueprint for data centers that are both environmentally friendly and cost-effective.

Another key objective is to drive innovation in green computing technology. This includes the development of hardware and software solutions that are energy efficient, from eco-friendly hardware components to software algorithms designed for reduced power consumption. The SIG will also explore the recycling and repurposing of computing resources as a means to reduce electronic waste.

This SIG will focus on utilizing big data analytics for environmental monitoring and sustainability studies. By analyzing large datasets related to climate, pollution, resource consumption, and other environmental factors, the group aims to contribute valuable insights to climate change research and support the development of effective green policies and practices.

A vital objective is to establish and nurture collaborations between industries, academic institutions, and environmental organizations. These partnerships are essential for driving innovation, sharing best practices, and implementing green big data solutions on a wider scale. The SIG will serve as a platform for knowledge exchange and joint projects among these diverse stakeholders.

The SIG is committed to raising awareness and educating data professionals and the general public about the significance of green big data practices. By highlighting the environmental impacts of data operations and promoting sustainable practices, the group aims to foster a culture of environmental responsibility within the big data community. This includes organizing workshops, seminars, and educational campaigns to disseminate knowledge and encourage sustainable practices in big data operations. We will focus on but not limited to the following topics:

• Greening big data
• Big data toward green applications
• Big data analytics for green IoT Systems
• Optimizing green edge computing orchestration
• Sustainable blockchain Applications
• Eco-friendly cooperative computing with IoT devices
• Ethical and sustainable management of smart device data
• Green multi-agent systems at the edge
• Game theory in green IoT and edge computing markets
• Joint management of cloud and edge for sustainability
• Energy efficiency in edge-assisted smart IoT systems
• Energy harvesting for sustainable bid data systems and smart devices
• Algorithm design for green edge-assisted smart systems
• Security and privacy in sustainable IoT services
• Sustainable multimedia services and management
• Design and experimental evaluation of green IoT and edge Technologies
• Educational Technologies integrating green big data systems

Need of SIG on Green Big Data

The establishment of a SIG on Green Big Data is essential for addressing the environmental challenges associated with big data technologies, fostering innovation in sustainable computing, and supporting the broader shift towards environmentally responsible practices in the tech industry.

• Addressing the Environmental Impact of Big Data: The rapid expansion of big data technologies has led to a significant increase in energy consumption and carbon emissions, primarily due to the energy-intensive nature of data centers and computing infrastructures. This environmental impact is becoming increasingly concerning, as it contributes to global warming and other environmental issues. A SIG on Green Big Data is crucial for developing strategies and technologies that minimize the environmental footprint of big data operations. By focusing on eco-friendly practices and technologies, this SIG can play a pivotal role in ensuring that the advancements in big data are aligned with environmental sustainability.
• Aligning with Sustainable Development Goals: In the current global context, where environmental awareness is more pronounced, industries are being urged to adopt sustainable development goals. A dedicated SIG on Green Big Data can guide the big data industry towards sustainable practices, ensuring that technological progress does not come at the expense of the environment. This alignment is essential not only for environmental preservation but also for the long-term viability of the industry.
• Promoting Energy Efficiency and Cost Savings: Energy-efficient solutions are not only beneficial for the environment but also offer significant cost advantages to organizations. By reducing energy consumption, these solutions can lead to substantial operational cost savings. The SIG on Green Big Data can be instrumental in promoting and developing energy-efficient technologies and practices, making big data operations more economically and environmentally sustainable.
• Fostering Innovation in Green Technologies: There is an urgent need for innovative solutions to address the environmental challenges posed by the proliferation of big data. The SIG on Green Big Data would serve as a platform for the research and development of such solutions, encouraging innovation in green technologies within the field. This focus on innovation is crucial for developing new methods and technologies that are both effective and environmentally friendly.
• Ensuring Regulatory Compliance and Demonstrating Corporate Responsibility: As environmental regulations become more stringent, companies are increasingly required to demonstrate their commitment to reducing their carbon footprint and adopting environmentally friendly practices. The SIG can offer guidance and best practices to help organizations comply with these regulations, thereby reinforcing their corporate responsibility and commitment to environmental stewardship.
• Raising Public Awareness and Education: There is a significant gap in public awareness and understanding of the environmental impacts of big data. The SIG can play an essential role in educational and outreach efforts, increasing awareness of these issues and promoting sustainable practices within the tech community and beyond. Educating professionals and the public about the importance of green big data practices is vital for fostering a culture of environmental responsibility in the industry.
• Encouraging Interdisciplinary Collaboration: The intersection of big data and environmental sustainability presents complex challenges that require collaborative and interdisciplinary solutions. By bringing together experts from various fields such as computer science, environmental science, engineering, and policy-making, the SIG on Green Big Data can facilitate the development of holistic and effective solutions to these challenges.

Contribution to the Society

The establishment of a Special Interest Group (SIG) on Green Big Data would have several valuable benefits for society:

• Environmental Protection and Sustainability: One of the most significant contributions of a Green Big Data SIG is its focus on reducing the environmental impact of big data technologies. By developing and promoting energy-efficient data processing methods, sustainable data center practices, and green computing technologies, this SIG can help mitigate the effects of climate change and promote environmental sustainability. These efforts contribute directly to the global goal of reducing carbon emissions and preserving natural resources, which is vital for the health and well-being of current and future generations.
• Economic Benefits: Implementing green big data solutions can lead to significant cost savings for businesses and government agencies. Energy-efficient technologies and sustainable practices often result in lower operational costs, especially in terms of energy consumption. Over time, these savings can be substantial, contributing to the economic health of companies and, by extension, the broader economy.
• Innovation and Job Creation: The push towards green big data technologies can stimulate innovation in multiple sectors, including IT, renewable energy, and environmental management. This innovation can lead to the creation of new jobs and industries, contributing to economic growth and providing new opportunities for workforce development. Additionally, the SIG can play a role in developing educational programs and training, preparing individuals for careers in these emerging fields.
• Public Health and Quality of Life Improvements: Reducing the environmental impact of big data operations can have direct and indirect benefits for public health. For instance, minimizing energy consumption in data centers can reduce the reliance on fossil fuels, thereby decreasing air pollution and its associated health risks. Furthermore, the application of big data in environmental monitoring can provide critical insights into pollution, climate change, and resource management, which are crucial for protecting ecosystems and public health.
• Enhanced Data Security and Privacy: With a focus on green big data, there is an opportunity to incorporate strong security and privacy measures as core components of sustainable data management practices. This ensures that as data usage grows, it does so in a way that respects individual privacy and enhances data security, which is increasingly important in a digital society.
• Educational and Awareness Building: By focusing on green big data, the SIG can play a significant role in educating the public and professionals about the importance of sustainable practices in technology. This heightened awareness can lead to more informed decisions by consumers, businesses, and policymakers, fostering a culture that values and prioritizes sustainability.
• Interdisciplinary Collaboration and Research: The challenges at the intersection of big data and environmental sustainability require solutions that span multiple disciplines. A Green Big Data SIG can facilitate collaboration among experts from different fields, leading to more comprehensive research, better understanding of complex issues, and more effective solutions.

Chair: Rongpeng Li, Zhejiang University, China, lirongpeng AT zju.edu.cn
Vice Chair: Biao Zhang, Google DeepMind, UK, biaojiaxing AT google.com
Vice Chair: Lan Zhang, Clemson University, USA, lan7 AT clemson.edu
Vice Chair: Charilaos Zarakovitis, Axon Logic, Greece, c.zarakovitis  AT axonlogic.gr
Advisor: Dusit Niyato, Nanyang Technological University, Singapore, dniyato AT ntu.edu.sg
Advisor: Honggang Zhang, honggangzhang AT zju.edu.cn
Advisor: Jun Zhang, Hong Kong University of Science and Technology, China, eejzhang AT ust.hk
Advisor: Mehdi Bennis, University of Oulu, Finland, mehdi.bennis AT oulu.fi
Advisor: Xianfu Chen, Shenzhen CyberAray Network Technology Company Ltd,
China, xianfu.chen AT ieee.org
Advisor: Yan Zhang, University of Oslo, Norway, yanzhang AT ieee.org
Advisor: Yonghui Li, University of Sydney, China, yonghui.li AT sydney.edu.au
Advisor: Yusheng Ji, National Institute of Informatics, Japan, kei AT klab.nii.ac.jp

Scope and Objectives
Artificial General Intelligence (AGI) is an implicit or explicit north-star goal since 1956 Dartmouth AI Conference. Given the rapid advancement of Machine Learning (ML) models, the concept of AGI has passed from being the subject of philosophical debate to one with near-term practical relevance. Nowadays, benefiting from the rapid progress and astonishing success in Natural Language Processing (NLP) and Computation Vision (CV), “sparks” of AGI are even regarded to be already present in the latest generation of Large Language Models (LLMs) and Large Vision Models (LVMs), with prominent examples like ChatGPT, Gemini, DALL-E and Sora. Meanwhile, techniques like generative Generative Adversarial Networks (GANs) and Diffusion models as well as scalable Transformers not only boost the arrival of these amazing Foundation Models (FMs), but also are seen as a transformative technology beyond shaping the AI field:

 FMs promise a tangible enhancement to wireless communications and networks by leveraging the generative capabilities as well as the multimodality nature of the data acquired in wireless networks. It promises to overcome long-standing difficulties such as low generality, limited performance gain, complicated
management, and inconvenient collaboration.

 The application of FMs for inference and decision-making purposes have demonstrated appealing results. It is widely anticipated that FM-empowered (connected) autonomous agents with embodied intelligence are expected to emerge with the astonishing capabilities of accomplishing tasks autonomously and coherently. Given these facts and visions, there is a clear need to establish a Special Interest Group (SIG) on AGI, Models, and Agents (AGILE) to address the emerging technical challenges therein. On one hand, it still requires ongoing significant efforts to deliver cost-effective AGI solutions. On the other hand, how to tackle the bloated parameters in FMs in edge and user equipment remain under-investigated. This SIG aims to organize and solicit researchers from both the academia and the industry to accelerate the study on AGILE. Tentative topics include, but are not limited to
– Artificial general intelligence techniques for AGILE
– Model design and training for AGILE
– Communication techniques in AGILE
– Communication and learning theory in AGILE
– Performance evaluation metrics of AGILE
– Collaboration mechanism in AGILE
– Network architecture for AGILE
– Security and privacy of AGILE
– Data collection and governance of AGILE
– Full-lifecycle management and orchestration of AGILE
– Architecture and protocol design & standarization of AGILE

Founding Members:
1. Changyang She, University of Sydney, Australia, changyang.she AT sydney.edu.au
2. Chengchao Liang, Chongqing University of Posts and Telecommunications, China, liangcc AT cqupt.edu.cn
3. Chenghui Peng, Huawei Technologies, China, pengchenghui AT huawei.com
4. Chungang Yang, Xidian University, China, chgyang2010 AT 163.com
5. Dong Wang, China Telecom, China, wangd5  AT chinatelecom.cn
6. Guangxu Zhu, Shenzhen Research Institute of Big Data, China, gxzhu AT sribd.cn
7. Gang Feng, University of Electronic Science and Technology, China, fenggang AT uestc.edu.cn
8. Haijun Zhang, Beijing Science and Technology University, China, zhanghaijun AT ustb.edu.cn
9. Jihong Park, Deakin University, China, jihong.park AT deakin.edu.au
10. Kai Yang, Beijing Institute of Technology, China, yangkai AT bit.edu.cn
11. Qiang Liu, University of Nebraska-Lincoln, USA, qiang.liu AT unl.edu
12. Qimei Chen, Wuhan University, China, chenqimei AT whu.edu.cn
13. Sai Mounika Errapotu, University of Texas at El Paso, serrapotu AT utep.edu
14. Shangmin Guo, University of Edinburgh, UK, s.guo AT ed.ac.uk
15. Xinyue Zhang, Kennesaw State University, USA, xzhang48 AT kennesaw.edu
16. Xueli An, Huawei Technologies, Germany, xueli.an AT huawei.com
17. Yong Xiao, Huazhong University of Science and Technology, China, yongxiao AT hust.edu.cn
18. Yuanming Shi, Shanghai Tech University, China, shiym AT shanghaitech.edu.cn
19. Yao Sun, University of Glasgow, UK, Yao.Sun AT glasgow.ac.uk
20. Yixiong Wei, Zhejiang Lab, China, yx_wei AT zhejianglab.com
21. Zhi Liu, The University of Electro-Communications, Japan, liuzhi AT uec.ac.jp
22. Zhijin Qin, Tsinghua University, China, qinzhijin AT tsinghua.edu.cn

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