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Publication Date: 2024 (TBD)
In recent years, there has been a growing need for sustainable development in the digital and industrial sectors in order to achieve carbon-neutrality by 2050. While artificial intelligence and the Internet of Things (AIoT) have provided numerous benefits in various domains, they also contribute significantly to global energy consumption and carbon dioxide emissions. Therefore, it is essential to develop sustainable solutions that include new energy infrastructure and distribution systems, energy-saving techniques, and energy-harvesting technologies. Furthermore, it is critical to design low-carbon and environmentally-friendly AIoT systems to reduce their environmental impact.
Sustainable computing is essential for energy-efficient IoT devices, which often operate under resource constraints. However, traditional AI methods are computationally intensive and can lead to high bandwidth overhead and latency, which are incompatible with most IoT devices. Even with edge computing, which offloads computation-intensive tasks to other devices, some operations must still be performed on IoT devices for privacy and security reasons. We need explore novel computer architectures, lightweight deep learning models, and other technologies that can enable sustainable computing for upcoming IoT applications driven by AI.
This special issue is devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects on sustainable computing for emerging AIoT applications. It also aims to provide worldwide researchers and practitioners an ideal platform to innovate new solutions targeting at the corresponding key challenges. High-quality original research and review articles in this area are expected.
The topics of interest include, but are not limited to:
For author information and guidelines on submission criteria, please visit the TSUSC's Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
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