Accepted Workshops and Tutorials at IoT2020

Human-Machine Learning Interaction Design: Challenges and Opportunities

Machine Learning (ML) is an area of research within Artificial Intelligence (AI) which uses complex mathematical models and data to recognize patterns and to aid decision making in different application areas. A key aspect of ML is the training when the machine is learning to detect patterns and make good classifications. While advances in the field have improved ML-methods, nonetheless humans also frequently have a crucial role to play in training of the models. In interactive machine learning (IML) the training occurs during an initial period and may also continuously occur based on user interaction. Yet how humans interact with ML models in training is not well understood. Connecting humans and ML as well as potentially massive datasets into interactive machine learning, we argue requires a multidisciplinary approach. This workshop aims to focus on humans interacting with models in training from the perspectives of HCI, Interaction Design, and Computer Science, by mapping UX and IML according to factors shared by both, with implications for future HCI research.

We invite researchers and practitioners in the areas of HCI, Interaction Design and Computer Science to apply for the workshop.

Please prepare a 3-4 page position paper in the ACM single-column Word Submission Template. Follow the instructions embedded in the template file for detailed guidance on applying the paragraph styles and including figures in your manuscript. Your manuscript will be produced in a single-column without any additional formatting. Then submit it using easychair submission page.

Position papers should be fitting within at least one of the following categories of submission:

  • Practical case studies of Human-Machine Learning interaction (e.g. case studies related to specific application areas, related to different interaction modalities, related to device/object/app design features, ML experiments and simulations, etc.)
  • Methodological exploration of understanding and designing Human-Machine Learning interaction. (e.g. user studies, system evaluations, Research through Design processes, etc.)
  • Theoretical explorations of Human-Machine Learning interaction (e.g. system design models, critical analysis, cultural analysis, etc.)
  • Demos and design implementations. Authors of the accepted position papers within this category will be asked to present their demos during the workshop.

Position papers will be peer-reviewed and the accepted position papers will be included in the companion proceedings and will be published in ACM digital library.

Maliheh Ghajargar (Malmö University, IoTaP research centre, Sweden)
Jan Persson (Malmö University, IoTaP research centre, Sweden)
Jeffrey Bardzell (Indiana University, Bloomington, USA)
Lars Holmberg (Malmö University, Sweden)
Agnes Tegen (Malmö University, IoTaP research centre, Sweden)

Deadline for submission: July 3, 2020 Extended to July 20, 2020

IoT-based Health Services and Applications (IoT-HSA-2020)

Mobile phones, smart watches, fitness trackers and a variety of Internet of Things (IoT) smart home devices constantly acquire data about our lives and our bodies. These data can be used to support healthcare and foster healthy and independent living, with potentially enormous benefits to our societies. Along with benefits, important challenges come in terms of quality and meaningfulness of the collected data, usability and long term acceptance, transparency, data protection and trustability. The aim of this workshop is to explore these fundamental questions with researchers and practitioners from academia and industry.

Update: A selection of the best papers will be invited to submit an extended version to Frontiers in Digital Health.

Dario Salvi (Malmö University, Sweden)
Francesco Potortí (CNR, Pisa, Italy)

Deadline for submission: July 15, 2020

Internet of Things for Emergency Management (IoT4Emergency)

The first international workshop on Internet of Things for Emergency Management (IoT4Emergency) will be held in Malmö, Sweden on October 6, 2020. The workshop is hosted by Malmö University, collocated with IoT2020.

IoT4Emergency aims to bring together academics and practitioners who use information and communication technologies to design emergency services. The accepted and presented papers will be published in the IoT2020 Conference Companion Proceedings, which will be indexed in ACM Digital Library.

The Internet of Things (IoT) has changed our approach to safety systems by connecting sensors and providing real-time data to managers and endangered people in emergencies, such as fires, earthquakes, floods, hurricanes, and even overcrowding. Furthermore, the recent situation regarding COVID-19 presents an unprecedented and extended emergency impacting humans’ social and economic life. IoTEM is a venue to gather researchers and practitioners who use information and communication technologies to address issues that happen before, during, and after an emergency.

The workshop covers all areas of emergency management that use the Internet of Things, cyber-physical systems, and pervasive computing. Topics of interest include, but are not limited to:

  • IoT-based hardware and software infrastructures for emergency handling
  • Sensing, networking, processing, and actuation for emergency management
  • IoT-based collaborative working during COVID-19
  • Social distancing systems for COVID-19
  • Humans (people, emergency managers, developers, firefighters, etc) in IoT systems
  • IoT-based emergency situational awareness
  • Disaster communication infrastructures, technologies, and services
  • IoT decision support technologies
  • IoT-based microservices design for emergency
  • IoT-related HCI for disaster preparation
  • Self-adaptation and self-adaptive architectures for disaster
  • Quality of Software-oriented disaster management IoT systems
  • IoT and social-behavioral analysis of endangered people
  • Big data processing for hazards
  • IoT-robotics systems for disaster handling
  • Remote sensing and cyber-physical systems
  • Wireless networks based solutions for disaster management
  • IoT-based modeling and simulation for crisis and disaster situations
  • Prediction and early warning systems
  • Crowd and queue management systems
  • Emergency traffic management systems
  • Real-world applications and field studies
  • Indoor/outdoor emergency evacuation

IoT4Emergency seeks the following types of papers: original technical papers, industry papers, position papers or practical case studies of no more than 8 pages.

Authors should consult ACM authors’ guidelines ( and use their proceedings templates, either for LaTeX or Microsoft Word, for the preparation of their papers. All submitted papers will undergo a rigorous peer review process. Papers will be selected based on originality, quality, soundness and relevance. All contributions must be original, not published, accepted or submitted for publication elsewhere.

Contributions should be submitted before the submission deadline using the online submission site:

All accepted papers will appear in the conference companion proceedings indexed by ACM. At least one author of an accepted contribution is required to register, present the work, and participate during the discussions at the workshop.

Important Dates
Submission: July 13, 2020 Extended to September 1, 2020
Notification: September 7, 2020 Extended to September 21, 2020
Camera ready: October 7, 2020 Changed to October 1, 2020

Henry Muccini (University of L’Aquila, Italy)
Julie Dugdale (University of Grenoble Alps, France)
Mahyar T. Moghaddam (University of L’Aquila, Italy – INRIA Grenoble, France)

Tutorial: Distributed Knowledge Graphs for the Web of Things

To address the interoperability problem on the Internet of Things and to realise the vision of an interlinked Web of Things, Knowledge Graph technologies from the Semantic Web and Linked Data stack can make a difference. In this tutorial, we cover foundational Knowledge Graph technologies that allow to perform data integration on the Internet of Things, and focus on Knowledge Graph technologies that allow to address the aspects of dynamics, distribution, behaviour, and control on the Internet of Things.

Andreas Harth (University of Erlangen-Nuremberg and Fraunhofer IIS-SCS)
Tobias Käfer (Karlsruhe Institute of Technology)

Contact: Tobias Käfer,

Tutorial: Internet of Things Systems: A Reconnaissance Flyover

This tutorial provides a comprehensive overview of the foundational principles, architecture, and design of IoT systems. It includes coverage and analysis of key components including sensors, edge, fog, communications, cloud, data processing with analytics ML and AI, security and management. It also provides a brief overview of IoT standards and commercial platforms. The emphasis is on providing a balanced treatment at roughly equal level of depth for all covered topics, based on the presenter’s bookInternet of Things: Concepts and Systems Design (Springer 2020). In addition to component analysis, major emphasis is on the system integration and how the various parts are put together and function as an ensemble to carry out the common objectives. Considerable attention is also devoted to IoT data and metadata representation in ways that foster semantic interoperability, aggregation, and reuse of data across specifications and system implementations.

Organizer: Dr. Milan Milenkovic, IoTsense LLC, Dublin, CA, USA

Contact: Milan Milenkovic,