D2C-ML&AI 2021 - The International Workshop on (D2C-ML&AI): Predictive and Learning Approaches based on Distributed-to-Centralized Machine Learning/Artificial Intelligence Techniques in Management of Large-Scale Internet of Things Networks in Smart Cities

The first series of the international 3SCity-E2C workshop was organized successfully, IEEE 3SCity-E2C, in conjunction with IEEE International Conference on Mobile Data Management (IEEE MDM 2020). The second series of the 3SCity-E2C workshop focuses on Machine Learning (ML), and Artificial Intelligence (AI) challenges in smart cities, mainly concentrate on distributed-to-centralized ML and AI techniques (D2C-ML&AI).
The workshop provides a forum to discuss the theoretical foundations and original technical contributions of building “Predictive and Learning Approaches based on ML and AI in Large-Scale Internet of Things (IoT) networks of Smart Cities.” We are interested in novel proposals based on Edge-to-Cloud computing solutions by bringing together industry, academia, engineers, and researchers. Proposals can contribute to all different domains of the Smart Cities (such as transportation, healthcare, energy, and grid) as well as different data analysis scopes (such as cybersecurity challenges and solutions for threat and attack detection, and resource allocation and consumption).
All accepted papers will be published in the proceedings of the main conference papers as part of the ACM International Conference Proceedings Series (ICPS) and will be indexed by the ACM Digital Library (CORE2018 Rank B). Also, authors of top papers will be invited to submit the extension of their quality work to the special issue of the MDPI journal – Networks Section, «Network Management: Advances and Opportunities.»
The challenge
Objectives:
Designing, implementation, and operation of integral solutions for building “Predictive and Learning Approaches based on Distributed-to-Centralized Machine Learning (ML) and Artificial Intelligence (AI) in Management of Large-Scale Internet of Things (IoT) networks in Smart Cities.”
Challenges:
Why is it necessary to consider and extend ML and AI techniques at the edge of IoT networks?
Data Growth in terms of “Size,” “Time,” and “Scale” (Large-Scale ICT and its Data Management): Exponential growth of city-data in widely distributed storage media of Smart Cities, as data is the main ingredient for ML and AI techniques and algorithms;
Data Privacy: Citizens and data stakeholders may not be willing to share their information in a public data storage (e.g., Cloud technologies platform);
General Data Protection Regulation (GDPR): GDPR is a regulation in the EU law on data protection and privacy. GDPR also addresses the transfer of personal data outside the EU and EEA areas;
Cybersecurity Concerns: In case of an attack at the centralized platform place (Cloud technologies) or multi-attacks at the IoT devices network will occur, how can we collect and send data/datasets to a centralized platform place to extract our knowledge requirements through data analysis/analytic and ML and AI techniques?
Complexities of running and managing complex ML and AI techniques at the edge of networks: Due to computational and memory limitations of IoT devices and low processing abilities at the edge of the Smart City networks, IoT devices are often not capable of running and managing complex ML and AI techniques and algorithms at the edge of the Smart City networks.
Cost of Cloud storage service: In most cases having Cloud storage can be quite expensive. Being charged year after year for a monthly or annual subscription can add up. Cloud storage providers know that customers will pay those high fees to have their data backed up and ultimately to have peace of mind.