The International Track on “Intelligent Internet of Things and Advanced Machine Learning Techniques for Smart Cities”

Special Themes:

  • Digital Twins for smart building, EMS, and neighborhoods 
  • Connected cars and vehicular networks solutions

in conjunction with The IEEE International Conference on Omni-layer Intelligent systems (IEEE COINS 2021), Barcelona, Spain, August 23-25, 2021

Download Call For Paper (CFP-3SCity-E2C 2021 Track)


Note that for each content you used on this page, please cite the below information in your reference list.

Reference template: Sinaeepourfard, Amir, “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,” 2nd Int’l. Wksp. Building Software Services in Smart City through Edge-to-Cloud orchestration, 2020; https://fmezen.no/3scity-e2c-workshop-2021/.


About IEEE COINS 2021 Conference:

COINS, Omni-layer Intelligent systems, includes a multi-disciplinary program from technical research papers, to panels, workshops, and tutorials on the latest technology developments and innovations. IEEE COINS will address all important aspects of the IoT ecosystem. IEEE COINS solicits papers and proposals accompanying submissions for presentations in the Vertical and Topical Tracks. See Call for Papers for more details. Conference proceedings will be published in the IEEE Xplore Digital library and indexing services.

Selected best contributions of IEEE COINS will be invited to submit expanded versions of their studies to IEEE IoTJ (IF=9.936) for review and potential publication.

About the Special Track of 3SCity-E2C:

The first and second series of the international 3SCity-E2C workshops were organized successfully, as shown details below:

The Track 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).

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?

  1. 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;
  2. Data Privacy: Citizens and data stakeholders may not be willing to share their information in a public data storage (e.g., Cloud technologies platform);
  3. 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;
  4. 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?
  5. 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.
  6. 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.
  • Introduction:

Smart Cities are known to be complex, large scale, and distributed environments that require growing use of IoT devices in various domains, such as transportation and healthcare. A Smart City is the result of the production of massive volumes of city-data from sensor networks and other physical data sources to non-physical data sources in the cities. This produced city-data has been stored in local data storage media in a city. Based on Cloud computing technologies, the produced city-data may shift from distributed local data storage media in a city to centralized data storage media (e.g., Cloud data storage media) somewhere outside the city in most cases. This data movement will be organized through particular update mechanism policies by the city manager. With regard to focus on designing “Predictive and Learning Approaches based on D2C-ML and AI in Management of Large-Scale IoT networks in Smart Cities,” data is the main ingredient for ML and AI techniques and algorithms. So, in the Large-Scale distributed IoT system in Smart Cities, before focusing on ML and AI techniques, it is essential to effectively and securely organize widely distributed IoT devices networks through a comprehensive ICT and its data management architecture (e.g., Distributed-to-Centralized Data Management: A New Sense of Large-Scale ICT Management of Smart City IoT Networks).

ML and AI algorithms and techniques are overgrowing and have been continuously changing and improving smart predictive approaches through different use cases, domains, and scenarios, such as Smart Cities. However, one of the main limiting factors which prevent many ML tasks is the need for huge and diverse training datasets. In Smart Cities, widely distributed IoT devices networks are continuously capturing various environmental city events and producing many data/datasets. Collecting many datasets may provide several facilities for the data stakeholders in the Smart Cities, but several challenges raise when the exponential growth of available city-data is stored in the local and centralized data storage media. Also, due to the 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. Finally, sometimes citizens and data stakeholders may not like to share their personal information with others in a public platform (e.g., Cloud technologies).

Recently new calls have been made to design large-scale IoT networks management in the Smart Cities from a very small scale (distributed) to a large scale (centralized) of the city. Edge-to-Cloud computing orchestration may offer a splendid solution to manage Large-Scale IoT networks in Smart Cities, such as data, resources, and software/services, as well as making it possible to apply complex ML and AL techniques and algorithms at the edge of networks. This solution also is useful to create and train datasets from Edge to Cloud concerning the privacy of city-data and other related issues.

Edge-to-Cloud orchestration may be put forward with different architectural layers in Smart Cities, such as Fog and cloudlet. Through different architectural layers from Edge-to-Cloud orchestration, there are several opportunities for building predictive model approaches based on distributed-to-centralized learning approaches to train the datasets through different ML and AI techniques. Therefore, this makes it possible to predict and manage different requirements of Smart Cities, such as resource allocation and consumption, and intelligent cyber-attacks predication.

In the case of the combined and hierarchical computing platform and architecture (i.e., Fog-to-Cloud or Fog-to-cloudlet-to-Cloud) from the distributed-to-centralized schema, it is necessary to define the architectural framework for predicting the Smart Cities requirements based on various ML and AI techniques. This framework makes numerous facilities to analyze varieties of datasets from different business domains. Therefore, it is essential to define and design that kind of architectural framework for combined Edge to Cloud technologies systems to manage and predict Smart Cities’ requirements, which is a yet demanding specific effort from the research community. Currently, two main approaches can be applied through different architectural layers of Edge-to-Cloud orchestration to train varieties of datasets as well as using different ML and AI techniques, as described below:

    1. Replicated Learning approach: Each IoT node/device produces data – which must be sent to the centralized node for training and prediction. Therefore, the centralized node receives and aggregates all data from IoT nodes and builds (a) training model(s), and only that model(s) is(are) shared from the centralized server to all distributed IoT nodes. In summary, replicated learning enables Edge devices to share their local data to the centralized node collaboratively, and the centralized node learns a machine learning model but keeps all the data on the storage media of the centralized node, instead of storing data on the local distributed nodes. Replicated Learning may come with the following advantages if the effective middleware platform/controller will be designed in between Edge-to-Cloud computing technologies:
        • It may make it possible to build more accurate ML models that are closer to the city-data sources in comparison with building ML models in the Cloud computing platform;
        • It may improve the predication level of resources closer to the city-data sources;
        • It may be possible to store city-data in the middleware platform instead of sending to the Cloud data storage;
        • It improves data privacy efficiency on the middleware platform;
        • This approach optimizes system bandwidth and data availability between geographically distributed data centers in the middleware platform.

       

    2. Federated Learning approach: Federated learning is a family of ML algorithms that has the core idea: a connected network exists in which there is a central server node. Each of the IoT nodes/devices creates data – that must be used for training and prediction. Each node trains a local model, and only that model is shared with the server, not the data. In summary, Federated learning enables Edge devices to learn an ML model collaboratively but keeping all the data on the IoT devices itself, instead of moving data to the Cloud computing technologies. Federated Learning has the following advantages:
      • Ability to build more accurate models faster;
      • Low latency during inference;
      • Privacy-preserving;
      • Improved energy efficiency of the devices.

Organization

Organizing Committee

Track Organizer and Idea Creator

Ask him about “Large-Scale IoT Management” and “Edge-to-Cloud Computing”

Amir Sinaeepourfard, Ph.D. is a “research associate” and a “Postdoctoral candidate” at the Norwegian University of Science and Technology (NTNU), Norway. He received a Ph.D. in Computer Architecture (focused on data and software management) with First Class Honors (Excellent CumLaude) from Technical University of Catalonia (UPC) in Spain, in 2018. He was a fellow of the FI-DGR fellowship program of the Generalitat de Catalunya in Spain during his Ph.D. studies. Based on his Ph.D. studies, he proposed several novel contributions for Large-Scale ICT management in smart cities through Edge-to-Cloud orchestration, e.g., “D2C-DM: Distributed-to-Centralized Data Management”. One of his publications, based on his Ph.D. thesis, received the IFIP Med-Hoc-Net paper of the year award for 2016. He has significant hands-on experiences in both academic research and organizing scientific events/seminars/workshops/conferences in his field. He is the “Workshop Organizer and Idea Creator” of a series of 3SCity-E2C: Building Software Services in Smart City through Edge-to-Cloud Orchestration international workshops, including “3SCity-E2C” and “D2C-ML&AI,” and “The ICT in Smart City Day event (ICT-SCity).”

His current research interest includes “Smart Cities, “IoT,” “Large-Scale IoT Management,” “Big Data Management,” “Cloud Computing,” “Edge Computing,” “Data and Software Management (including Centralized and Distributed).”

His publication lists> Google Scholar

Scientific Committee

  • Pierluigi Salvo Rossi, Norwegian University of Science and Technology (NTNU), Norway
  • Mamoun Alazab, College of Engineering, IT and Environment, Australia
  • Antonio J. Jara, University of Applied Sciences Western, Switzerland
  • Alireza Jolfaei, Macquarie University, Australia
  • Antonio Salis, Engineering Sardegna, Italy
  • Phu Nguyen, SINTEF, Norway
  • Vinayakumar Ravi, University of Cincinnati, USA
  • Dirk Ahlers, Norwegian University of Science and Technology (NTNU), Norway
  • Amirhosein Taherkordi, Universitetet i Oslo (UiO), Norway
  • Jens Jensen, UK Research and Innovation-Science and Technology Facilities Council (UKRI-STFC), UK
  • Shuaib Siddiqui, i2CAT Foundation, Spain
  • Deepak Puthal, Newcastle University, UK
  • Mohamed Hamdy, Norwegian University of Science and Technology (NTNU), Norway
  • Alexander Norta, TalTech, Estonia
  • Octavio Loyola-González, Tecnologico de Monterrey, Mexico
  • Shehenaz Shaik, Auburn University, Auburn, AL, USA
  • Suman Sankar Bhunia, PwC Inc., India
  • Vitor Barbosa Souza, Universidade Federal de Viçosa (UFV), Brazil
  • Saad Qaisar, National University of Sciences and Technology (NUST), Pakistan
  • Ali Dorri, Queensland University of Technology (QUT), Australia
  • Souvik Sengupta, i2CAT, Spain
  • Gowri Sankar Ramachandran, University of Southern California, USA
  • Sarang Kahvazadeh, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain
  • Qaisar Shafi, National University of Sciences and Technology (NUST), Pakistan

Keynote speakers

TBA

Partners

NTNU – Norwegian University of Science and Technology

NTNU is a university with an international focus, with headquarters in Trondheim and campuses in Ålesund and Gjøvik.

NTNU has a main profile in science and technology, a variety of programs of professional study, and great academic breadth that also includes the humanities, social sciences, economics, medicine, health sciences, educational science, architecture, entrepreneurship, art disciplines, and artistic activities.

www.ntnu.edu

ZEN – Zero Emission Neighbourhoods in Smart Cities

The ZEN Research Centre conducts research on Zero-Emission Neighbourhoods (ZEN) in smart cities.

The main goal is to develop solutions for future buildings and neighbourhoods with no greenhouse gas emissions and thereby contribute to a low carbon society.

https://www.fmezen.no/

Submission

Topics

The special track provides a forum to discuss the theoretical foundations and original technical contributions of building “Predictive and Learning Approaches based on Machine Learning (ML) and Artificial Intelligence (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). We invite submissions of the unpublished work on the following topics (but not limited to):

Sub-Track I- Large-Scale IoT Management & ML/AI

Leader of Sub-Track I> Amir Sinaeepourfard, Ph.D., NTNU, Norway

• ML and AI techniques for Large-Scale IoT networks management of Smart Cities;
• Federated and Replicated Learning in Large-Scale IoT networks management of Smart Cities;
• Scalable and reliable framework for Federated and Replicated Learning;

Task 1- Different business domains of smart cities & ML/AI>

• Federated and Replicated learning in the different business domains of Smart Cities;
• In particular theme interest, Federated and Replicated learning for the below smart cities domains.

  • Digital Twins for smart building, EMS, and neighborhoods>

          Leader> Mohamed Hamdy, Ph.D., NTNU, Norway

  • Connected cars and vehicular networks solutions> 

          Leader> Vitor Barbosa Souza, Ph.D., UFV, Brazil

Task 2 – Edge-to-Cloud orchestration & ML/AI>

Leader of Task 2> Sarang Kahvazadeh, CTTC, Spain

• Hybrid and collaborative architectural models for learning and predict the requirement of Smart Cities through different layers from Edge-to-Cloud computing technologies;
• Developing applications and software services of Large-Scale IoT networks in Smart Cities based on the availability of data in distributed-to-centralized data storage media and their related learning models and orchestration;
• Distributed system and data and ICT architecture management design and implementation for Smart Cities.

Task 3- Performance and Economic Efficiency & ML/AI

Leader of Task 3> Antonio Salis, Engineering Sardegna, Italy

• Performance efficiency in comparison of different learning and predict approaches, including Cloud computing technologies and/or Edge-to-Cloud computing technologies;
• Economic efficiency in comparison of different learning and predict approaches, including Cloud computing technologies and/or Edge-to-Cloud computing technologies.

Sub-Track II- Cybersecurity & ML/AI

Leader of Sub-Track> Alireza Jolfaei, Ph.D., Macquarie University, Australia

• On-device privacy-preserving Learning;
• Security and privacy aspects of Federated and Replicated Learning;
• Combating cyberattacks using AI through Edge-to-Cloud networks, including adopting traditional machine learning methods and existing deep learning solutions;
• Distributed and distributed-to-centralized learning approaches to predict different IoT cybersecurity requirements of Smart Cities, such as anomaly detection challenges (threat and attack detection).

Task 1- Malware & ML/AI

Leader of Task 1> Mamoun Alazab, Ph.D., CDU’s College of Engineering, Australia

• Malware detection/treatment for Large-Scale IoT networks via Federated and Replicated Learning approaches.

Task 2- Blockchain & ML/AI

Leader of Task 2> Ali Dorri, Ph.D., QUT, Australia

• Blockchain for Federated and Replicated Learning.

Sub-Track III- Resource Management & ML/AI

Leader of Sub-Track 3> Souvik Sengupta, Ph.D., i2CAT, Spain

• Distributed and distributed-to-centralized learning approaches to predict different IoT resource requirements of Smart Cities, such as optimization of resource allocation and consumption.

Important dates

  • Deadline for Track Papers: April 30, 2021
  • Decision on Acceptance/Rejection of the Track Papers: June 6, 2021

Submission instruction

Please visit the organizer page for further information, https://coinsconf.com/cfp2021/.

Program

TBA

Venue

Please visit this link for further information.

Contact

For further information, please contact us at a.sinaee@ntnu.no. Please put the title of your email “3SCity-E2C 2021 Track-Question”.