Welcome to AIoT 2024!

Nowadays, the impressive proliferation of IoT devices (predicted to reach 30 billion by 2030), able to monitor several real-world processes and environments, is driving the development of extreme analytics for business decisions based on the vast amount of data collected by smart objects. Indeed, emerging wireless technologies, such as 5G and LPWAN, are enabling the possibility to easily and efficiently connect tiny devices, which are also equipped with heterogeneous computational capacity, varying from smartphones to micro-controllers, deployed over large geographical areas. 

In such a context, emerging learning mechanisms, such as distributed and federated learning, can be a promising alternative to traditional centralized analytics. 

The Second International ACM MobiHoc Workshop on the Integration between Distributed Machine Learning and the Internet of Things (AIoT) is specifically meant to gather new ideas, contributions, and experiences on the integration of Distributed and Federated Machine Learning with long-range IoT systems. 

Program

Keynote: Transforming Industries Through Real-World Deployments leveraging IoT, AI/ML and (beyond) 5G technologies

Speaker: Sokratis Barmpounakis,  Wings ICT Solutions, Greece

In today’s rapidly evolving digital landscape, the convergence of IoT, AI/ML, and (beyond) 5G (B5G) technologies is revolutionizing industries across various domains. This keynote will explore how innovative solutions developed by WINGS ICT Solutions SA are driving transformation in sectors like Environmental Monitoring, Industry 4.0, Transport & Logistics, Energy, etc. By addressing key challenges related to social, economic and environmental sustainability, such as energy efficiency, trust, computational cost optimization, performance, communication reliability, etc. these solutions harness cutting-edge AI/ML techniques to manage scarce resources and enhance the sustainability and reliability of IoT deployments.

The presentation will also highlight the role of B5G technologies, including exposure of infrastructure and network capabilities towards Compute- and AI-as-a-Service, and how flexible Cloud Continuum enablers pave the way for advanced Distributed and Federated AI paradigms for real-world demands. Attendees will gain insight into practical applications of these technologies, with case studies from commercial, as well as research projects demonstrating their impact across the IoT ecosystem.

Speaker Bio

 
Dr. Sokratis Barmpounakis holds a Ph.D. in “Context-based Resource Management and Slicing for SDN-enabled 5G Smart, Connected Environments” (2018). He obtained his Engineering Diploma in Electrical and Computer Engineering, from the National Technical University of Athens (NTUA) in 2010. 

Call for Papers

Nowadays, the impressive proliferation of widespread wireless technologies, such as 5G and LPWAN, are enabling the possibility to easily and efficiently connect tiny IoT devices, varying from smartphones to micro-controllers, deployed over large geographical areas.

In such a context, emerging learning mechanisms, such as distributed and federated learning, can be a promising alternative to vanilla centralized approaches. With such techniques, it is possible to minimize the amount of unnecessary data streamed for processing and to move decisions closer to the data sources thus enabling faster, ideally real-time, analytics.

However, the integration between Distributed/Federated Learning mechanisms and the Internet of Things poses a series of whole new challenges, such as the compression of models to be transmitted over unreliable channels with a limited amount of bandwidth, the optimization of the network lifetime, the orchestration and management of the scarce computation, communication and storage resources, the speed-up of the distributed learning process, and so on.

AIoT, the Second International ACM Mobihoc Workshop on the Integration between Distributed Machine Learning and the Internet of Things is specifically meant to gather new ideas, contributions, and experiences on the integration of Distributed and Federated Machine Learning with long-range IoT systems. Topics include, but are not limited to:

• Efficient Machine Learning in the IoT

• Hardware for Machine Learning and Deep Learning in the IoT

• Network Layer technologies to support Machine Learning in the IoT

• Protocols to support Distributed Machine Learning in the IoT

• Edge computing and IoT for distributed/federated learning

• Experimental validation of distributed machine learning for IoT

• Testbeds and tools for distributed machine learning in IoT

• Privacy-preserving data sharing and aggregation in distributed/federated learning

• Datasets and applications of distributed/federated learning in IoT (eg. spectrum sensing, healthcare, smart cities, and transportation)

• Scalability and performance issues in IoT and distributed/federated learning

• Emerging trends, challenges, and future directions in IoT and distributed/federated learning

Important Dates

• Paper submission: July 19, 2024 August 9, 2024 [FIRM]  (11:59pm EDT) 

• Acceptance notification: August 17, 2024 

• Camera ready and registration: August 30, 2024

• Workshop date: October 14, 2024

Submission Instructions

Papers should be submitted via the HotCRP submissing website (https://aiot24.hotcrp.com/).

Submissions must be original, unpublished work, and not currently under consideration elsewhere. Papers should not exceed 6 pages (US letter size) double column including figures, tables, and references in standard ACM format. Papers must be submitted electronically in printable PDF form. Templates for the standard ACM format can be found at this link: http://www.acm.org/publications/article-templates/proceedings-template.html . If you are using LaTeX, please refer to the sample file “sample-sigconf.tex” after you download the .zip templates file and unzip it. Note that the document class “\documentclass[sigconf]{acmart}” should be used. No changes to margins, spacing, or font sizes are allowed from those specified by the style files. Papers violating the formatting guidelines will be returned without review.

All submissions will be reviewed using a single-blind review process. The identity of referees will not be revealed to authors, but author can keep their names on the submitted papers, on figures, bibliography, etc.

Dual Submission Policy

Accepted papers will appear in the conference proceedings published by the ACM. Warning: It is ACM policy not to allow double submissions, where the same paper is submitted to more than one conference/journal concurrently. Any double submissions detected will be immediately rejected from all conferences/journals involved.