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EMSICC 2024 : International Workshop on Energy Management for Sustainable Internet-of-Things and Cloud Computing

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Link: https://emsicc.github.io/2024/
 
When Aug 19, 2024 - Aug 21, 2024
Where Vienna, Austria
Submission Deadline Apr 30, 2024
Notification Due May 25, 2024
Final Version Due Jun 10, 2024
Categories    internet of things   energy-aware   cyber-physical systems   machine learning
 

Call For Papers

Date : August 19-21, 2024
Location : Vienna, Austria

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CALL FOR PAPERS
EMSICC 2024: 9th International Workshop on Energy Management for Sustainable Internet-of-Things and Cloud Computing
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August 19-21, 2024
Vienna, Austria

In conjunction with the 11th IEEE International Conference on Future Internet of Things and Cloud (FiCloud 2024)

Workshop website: https://emsicc.github.io/2024/

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IMPORTANT DATES
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Paper Submission: April 30, 2024 (11:59pm Anywhere on Earth)
Author Notification: May 25, 2024
Final Manuscript Due: June 10, 2024
Workshop: August 19-21, Vienna, Austria

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SUBMISSIONS
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Authors are requested to electronically submit a PDF paper (maximum 6 pages in IEEE two-column format: https://www.ieee.org/conferences/publishing/templates.html).

Use the following link for submission: https://easychair.org/conferences/?conf=emsicc2024

The proceedings of the workshop will be published by the Conference Publishing Services (CPS)

Please email any inquiries to emsicc.ws@gmail.com

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AIMS AND SCOPE
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With the emergence of wireless communications, geolocation technologies and cloud computing, innovative applications are designed for the Internet of Things (IoT) and Cyber-physical systems, targeting intensive data computations and virtualization technologies. Due to their constraints in terms of energy, computational power, memory, high mobility, sporadic connectivity, and sometimes security constraints, some smart devices need to outsource data storage, application hosting, and computation to the Cloud. Hence, the IoT and Cyber-physical systems require efficient and adaptive energy management solutions that optimize energy consumption through energy-aware communications protocols, scheduling methods, self-organization mechanisms, offloading techniques, and security solutions, among others. We encourage submissions around the development of energy-aware methodologies and technologies that improve the collection, transmission, storage, and processing.

The potential effects of these technologies on the machine learning processes that are executed in the cloud using the data collected by IoT and Cyber-physical systems should be considered and analyzed. In the presence of energy constraints, the machine learning processes may be hindered and see their performances deteriorate. Many factors can be at the origin of these performance degradations. Energy-aware strategies, e.g., intermittent transmissions, decrease in sensing precision, reduction of the sampling frequency, etc., may compromise the availability of data in a timely manner and increase uncertainty in the data. Furthermore, in the case of distributed machine learning, energy-aware strategies may lead to heterogeneity in terms of the updates that the cloud receives from the distributed local IoT and Cyber-physical systems. For example, standard aggregation-based machine learning algorithms, like federated learning, are highly impacted by the heterogeneity in terms of the transmission rates of the IoT and Cyber-physical systems. All these aspects have an impact on the machine learning processes that are performed in the cloud, however, it is not yet well understood how it manifests and how it could be taken into account. This workshop provides a forum to discuss these aspects and devise new directions for future research.


Alternative energy sources available in the IoT and Cyber-physical systems’ surrounding environments could be used to achieve perpetual functioning without replacing or recharging batteries as often through energy harvesting. Machine learning is key to developing such strategies, e.g., analyzing patterns of energy consumption to adapt energy harvesting strategies, etc. Furthermore, energy management and optimization are also concerns for cloud data centers, which need efficient management of power and performance for computing and air conditioning and of environmental impact. These factors largely improve cost versus energy optimization, which can be achieved through various approaches like optimization, computational intelligence, and machine learning.


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TOPICS OF INTEREST
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This workshop is intended to provide a forum for discussing a wide range of problems related to energy-aware, energy-efficient management solutions for designing distributed computing platforms (software and hardware) for the IoT, Cyber-physical and Cloud Computing systems.

Prospective authors are invited to submit original, previously unpublished work, reporting on novel and significant research contributions, on-going research projects, experimental results and recent developments related to, but not limited to, the following topics:

- Impact of energy-aware strategies on machine learning in the cloud
- Impact of energy-aware strategies on federated learning
- Energy-aware/energy harvesting scheduling algorithms using intelligence
- Adaptive middleware for energy-efficient computing
- Instrumentation and measurement of energy-efficient computing and networking
- Enhanced performance and QoS in energy-efficient systems
- Resource management in large infrastructures (such as data centres) and in power/energy-constrained systems
- Data management in energy-efficient systems
- Sensing, monitoring, control, and management of energy systems
- Modelling, control, and architectures for renewable energy generation resources
- Privacy and security in energy-aware platforms
- Metrics, benchmarks, interfaces, and tools to consider energy dimension
- Energy-aware hardware platforms
- Specifications and validation of energy-aware platforms
- Energy efficiency and virtualization in Cloud of Things
- Energy services for smart cities
- Intelligent optimisation and computational intelligence for IoT devices
- Machine learning approaches for energy management
- Learning of patterns of energy deployment in smart applications
- Prediction of anticipated energy consumption.


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ORGANIZING COMMITTEE
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- Aomar Osmani, Univ. Sorbonne Paris-Nord
- Samia Bouzefrane, Le Cnam
- Massinissa Hamidi, Univ. Evry Paris-Saclay

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