Energy Efficient Trustworthy Sustainable Edge-Cloud Computing
In conjunction with IEEE Cloudnet 2023, 3 November 2023, New York Area, USA
Nowadays, due to the advancement of the internet, several emerging areas (e.g., cyber-physical systems, sensors, healthcare, the Internet of Things) have become much popular. These geographically dispersed devices are connected with a network. Exchanging data through these networks faces several issues such as network breakdown with high latency rate, security, and privacy of data etc. Due to the increased complexity of such networks, edge computing and advanced cryptographic technologies can play an important role and provide an optimized solution to build a secure and privacy-preserving network. Because most of the standard encryption algorithms are well suited for the desktop environment, many of them are not well suited to constrained devices such as IoT. Therefore, we also need a lightweight advanced cryptographic solution to secure the data. Lately, there is a significant advancement of eco-friendly hardware and software solutions, ensuring enhanced consumer experiences while promoting energy management and better quality of service using edge cloud paradigm. As technology evolves, the next generation of networking must address the challenges of energy efficiency, trustworthiness, and sustainability.
This workshop aims to bring together researchers, practitioners, and industry experts to discuss and explore innovative solutions that leverage Distributed and Resilient Machine Learning (DRML) approaches to achieve data integrity, security, and reliability, without compromising the consumer experience.
The key objective of the workshop theme is to encompass the following issues:
- To understand the importance of green hardware and software solutions in the next generation of consumer electronics, and their role in energy management and e-waste reduction.
- To explore the potential of Distributed and Resilient Machine Learning (DRML) approaches in providing trustworthy, scalable, and energy-efficient solutions for Edge-Cloud computing
- To discuss the challenges and opportunities in developing distributed green applications and software solutions that meet organizations' requirements while promoting energy efficiency.
- To investigate methods for ensuring data integrity, security, and resilience in green consumer electronics systems, including protection against malicious attacks and breaches.
Topics of interest include but are not limited to the following:
- Green hardware and software solutions for energy management in Edge Cloud Computing
- Distributed and Resilient Machine Learning (DRML) for trustworthy and energy-efficient applications
- Data integrity and security in green Edge Cloud Computing
- Resilience, availability, confidentiality, scalability, and robustness in DRML solutions
- Privacy-preserving techniques in energy-efficient Edge Cloud Computing
- Digital Twin for Edge Cloud Computing
- Edge Cloud Computing for 5G&B
- Edge Cloud Computing for Healthcare Applications
- Edge Cloud Computing for Smart Grid Applications
- Edge Cloud Computing for Smart Transportations
- Edge Cloud Computing for Industrial IoT
- AI/ML for Edge Cloud Computing
- Edge-Intelligence (EI): AI/ML driven Edge for Smart Infrastructures
- Emerging Architecture/Framework for Intelligent Edge Cloud Computing
- QoS modeling, measurement, and optimization of Edge Cloud Computing
- Edge Cloud Computing and SDN for Smart Infrastructures
- Paper Submission Deadline: September 15, 2023
- Notification of Acceptance: September 20, 2023
- Camera-Ready Paper Deadline: September 25, 2023
- Uttam Ghosh, Meharry Medical College, TN, email@example.com
- Sachin Shetty, Old Dominion University, VA, firstname.lastname@example.org
- Al-Sakib Khan Pathan, United International University (UIU), Bangladesh, email@example.com
Technical Program Committee
- Sourav Banerjee, Kalyani Government Engineering College, India
- Pushpita Chatterjee, Howard University, USA
- Debashis Das, Kalyani University, India
- Deepak Tosh, University of Texas at El Paso, USA
- Suman Bhunia, Miami University, USA
- Pravin Mundra, Medtronic, USA
- Sidheswar Routray, Indrashil University, Mehsana, India.
- Amrit Mukherjee, University of Bohemia, Czech Republic.