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Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (5 May 2021) | Viewed by 18804

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Special Issue Editors

Dr. Francesco Lelli

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Guest Editor

Department of Management, University of Tilburg, 5041 Tilburg, The Netherlands
Interests: business process integration; service-oriented architecture
Special Issues, Collections and Topics in MDPI journals

Dr. Stefano Modafferi

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Guest Editor

Department of Electronics and Computer Science, University of Southampton, SO16 7NS Southampton, UK
Interests: information systems; information modeling; computer science; software architecture; databases

Special Issue Information

Dear Colleagues,

At present, complex IoT systems encompass intelligent devices that produce a large amount of data. These systems can be studied from different perspectives and at any level of the typical multilayer stack used to manage them. These systems are increasingly autonomous and able to make decisions. The more systems become autonomous, the more it becomes relevant to have software and models, but also report on case studies and best practices. This enables us to understand and manage the human factor and the implications that the increased system autonomy has on people.

This Special Issue looks at this human factor in the processes across the entire range, from data production to the business models used to monetize the created value. In this context, human–computer interaction therefore has a broad meaning that includes all the different aspects of having humans in the loop, somehow involved in the processes executed over and by the complex systems.

In this respect, we welcome contributions that aim to share technologies, policies, or heuristics from either a theoretical or practical point of view. We also welcome case studies for any relevant vertical domain. Cases from Health, Factory of Future, Media, and Fintech are particularly welcome.

Potential topics include, but are not limited to:

HCI Trust and Acceptability

Reducing information asymmetry to increase human trust in AI-based systems;
Models and experiences of risk analysis in HCI systems to increase human trust;
Models and experiences of users’ understanding and acceptability of complex and AI-based ICT systems;
Integration of explainable AI into established workflows and industrial processes.

HCI Education/learning

Innovative instruments, processes, and experiences to educate workers of the immersive use of AI-based complex systems;
Innovative instruments, processes, and experiences to educate the general public of everyday life in a pervasive AI populated world;
AI-based learning instruments to explain and increase the acceptability of complex AI-based systems.

HCI Cross-field support.

Novel experiences, processes, and models to bridge the gap between social science and ICT for the complete assessment of HCI systems;
Instruments and experience to support data scientists in the design and configuration of AI-based decision support systems.

HCI Practical Experiences and Business Cases.

Impact on business models of users in the multiple capacities of data producers, data processors, and data consumers in complex AI-based systems;
Industrial cases showing innovative aspects in HCI;
Insights and findings resulting from survey analyses of HCI experiences.

Dr. Francesco Lelli
Dr. Stefano Modafferi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Cloud computing
Internet of Things
Edge computing
Industry 4.0
Trust
Human–computer interaction
Information modeling
Risk analysis
System design
Industrial cases
Business models
Explainable AI
Intelligent systems
eLearning in and for IoT systems
Education for ICT practitioners

Published Papers (6 papers)

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Research

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15 pages, 3640 KiB

Open AccessArticle

Vision System Experimentation in Furniture Industrial Environment

by Gurbaksh Bhullar, Simon Osborne, María José Núñez Ariño, Juan Del Agua Navarro and Fernando Gigante Valencia

Future Internet 2021, 13(8), 189; https://doi.org/10.3390/fi13080189 - 23 Jul 2021

Cited by 6 | Viewed by 1805

Abstract

The integration of devices that support manufacturing activities and the interaction of workers with these devices in production plants, leads to potential benefits in the industrial environment. Problems, bottlenecks and improvement opportunities throughout production times need to be detected, analyzed and prioritized in order to select the most suitable solutions and address them properly. The integration of particular devices supports the manufacturing process and prevents the need for contingency planning; it also increases the quality of the produced goods, which leads to higher customer confidence and satisfaction. The scope of this article focuses on the development and experimentation of a vision system for the recognition of product components in order to support the classification of such items by the users working in a particular area of the production line. Even if the proposed solution presents a low level of human interaction and innovation, the objective of this paper is to demonstrate how the proposed classification system brings valuable benefits to the overall manufacturing process in a traditional furniture environment, with the inherent advantage that workers can perform this task in a more guided and riskless manner. The Overall Equipment Effectiveness (OEE) approach was adopted to measure the benefits of the solution, which are described in article. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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24 pages, 4828 KiB

Open AccessArticle

An Intelligent System to Ensure Interoperability for the Dairy Farm Business Model

by Adina Cretan, Cristina Nica, Carlos Coutinho, Ricardo Jardim-Goncalves and Ben Bratu

Future Internet 2021, 13(6), 153; https://doi.org/10.3390/fi13060153 - 12 Jun 2021

Cited by 2 | Viewed by 1961

Abstract

Picking reliable partners, negotiating synchronously with all partners, and managing similar proposals are challenging tasks for any manager. This challenge is even harder when it concerns small and medium enterprises (SMEs) who need to deal with short budgets and evident size limitations, often leading them to avoid handling very large contracts. This size problem can only be mitigated by collaboration efforts between multiple SMEs, but then again this brings back the initially stated issues. To address these problems, this paper proposes a collaborative negotiation system that automates the outsourcing part by assisting the manager throughout a negotiation. The described system provides a comprehensive view of all negotiations, facilitates simultaneous bilateral negotiations, and provides support for ensuring interoperability among multiple partners negotiating on a task described by multiple attributes. In addition, it relies on an ontology to cope with the challenges of semantic interoperability, it automates the selection of reliable partners by using a lattice-based approach, and it manages similar proposals by allowing domain experts to define a satisfaction degree for each SME. To showcase this method, this research focused on small and medium-size dairy farms (DFs) and describes a negotiation scenario in which a few DFs are able to assess and generate proposals. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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29 pages, 1308 KiB

Open AccessArticle

H2O: Secure Interactions in IoT via Behavioral Fingerprinting

by Marco Ferretti, Serena Nicolazzo and Antonino Nocera

Future Internet 2021, 13(5), 117; https://doi.org/10.3390/fi13050117 - 30 Apr 2021

Cited by 7 | Viewed by 2280

Abstract

Sharing data and services in the Internet of Things (IoT) can give rise to significant security concerns with information being sensitive and vulnerable to attacks. In such an environment, objects can be either public resources or owned by humans. For this reason, the need of monitoring the reliability of all involved actors, both persons and smart objects, assuring that they really are who they claim to be, is becoming an essential property of the IoT, with the increase in the pervasive adoption of such a paradigm. In this paper, we tackle this problem by proposing a new framework, called H2O (Human to Object). Our solution is able to continuously authenticate an entity in the network, providing a reliability assessment mechanism based on behavioral fingerprinting. A detailed security analysis evaluates the robustness of the proposed protocol; furthermore, a performance analysis shows the feasibility of our approach. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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15 pages, 3609 KiB

Open AccessArticle

Remote Monitoring Model for the Preoperative Prehabilitation Program of Patients Requiring Abdominal Surgery

by Khalid Al-Naime, Adnan Al-Anbuky and Grant Mawston

Future Internet 2021, 13(5), 104; https://doi.org/10.3390/fi13050104 - 22 Apr 2021

Cited by 6 | Viewed by 2034

Abstract

Physical fitness and level of activity are considered important factors for patients with cancer undergoing major abdominal surgery. Cancer patients with low fitness capacity are at greater risk of postoperative complications, longer hospital stays, and mortality. One of the main challenges facing both healthcare providers and patients is to improve the patient’s physical fitness within the available short period (four to six weeks) prior to surgery. Supervised and unsupervised physical prehabilitation programs are the most common recommended methods for enhancing postoperative outcomes in patients undergoing abdominal surgery. Due to obstacles such as geographical isolation, many patients have limited access to medical centers and facilities that provide onsite prehabilitation programs. This article presents a review of the literature and the development of a model that can remotely monitor physical activities during the prehabilitation period. The mixed prehabilitation model includes the identification of fundamental parameters of physical activities (type, intensity, frequency, and duration) over time. A mathematical model has been developed to offer a solution for both the healthcare provider and patients. This offers the opportunity for physicians or physiotherapists to monitor patients performing their prescribed physical exercises in real time. The model that has been developed is embedded within the internet of things (IoT) system, which calculates the daily and weekly efforts made by the patients and automatically stores this in a comma-separated values (CSV) file that medical staff can access. In addition, this model allows the patient to compensate for missed prescribed activity by adding additional efforts to meet the prehabilitation requirements. As a result, healthcare staff are provided with feedback on patient engagement in prescribed exercise during the period of the prehabilitation program. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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Review

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36 pages, 886 KiB

Open AccessReview

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

by Romy Müller, Franziska Kessler, David W. Humphrey and Julian Rahm

Future Internet 2021, 13(6), 156; https://doi.org/10.3390/fi13060156 - 17 Jun 2021

Cited by 4 | Viewed by 2341

Abstract

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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20 pages, 522 KiB

Open AccessReview

Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies

by Brian Pickering

Future Internet 2021, 13(5), 132; https://doi.org/10.3390/fi13050132 - 18 May 2021

Cited by 10 | Viewed by 6766

Abstract

To use technology or engage with research or medical treatment typically requires user consent: agreeing to terms of use with technology or services, or providing informed consent for research participation, for clinical trials and medical intervention, or as one legal basis for processing personal data. Introducing AI technologies, where explainability and trustworthiness are focus items for both government guidelines and responsible technologists, imposes additional challenges. Understanding enough of the technology to be able to make an informed decision, or consent, is essential but involves an acceptance of uncertain outcomes. Further, the contribution of AI-enabled technologies not least during the COVID-19 pandemic raises ethical concerns about the governance associated with their development and deployment. Using three typical scenarios—contact tracing, big data analytics and research during public emergencies—this paper explores a trust-based alternative to consent. Unlike existing consent-based mechanisms, this approach sees consent as a typical behavioural response to perceived contextual characteristics. Decisions to engage derive from the assumption that all relevant stakeholders including research participants will negotiate on an ongoing basis. Accepting dynamic negotiation between the main stakeholders as proposed here introduces a specifically socio–psychological perspective into the debate about human responses to artificial intelligence. This trust-based consent process leads to a set of recommendations for the ethical use of advanced technologies as well as for the ethical review of applied research projects. Full article

(This article belongs to the Special Issue Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing)

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