Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Cyber Risk in the Industrial Internet of Things
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Cyber Risk in the Industrial Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (1 November 2021) | Viewed by 3010

Special Issue Editor

Prof. Dr. Pete Burnap

E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Interests: security and protection; human-centered computing; modeling structured, textual and multimedia data; data mining; machine learning

Special Issue Information

Dear Colleagues,

The next generation of industrial systems is going to be data-driven. Sensor data driven by the Internet of Things, both inside and outside of factories, will provide a digital thread connecting the factory floor with its products and customers. These “chatty” factories present immense opportunities for innovation, from improving products based on their use, to digital twins that fuse product redesign with intelligent re-manufacturing, all connected via industrial optimisation, and relationships between intelligent robotics and their human counterparts that are yet to be determined.

The opportunities are clear. However, with digital innovation comes inherent cyber risk. Sensor data from the wild can only be used if it flows into decision-making processes linked to industrial modernisation. How do we know that the integrity of sensor data is not being corrupted, either deliberately or otherwise? What are the cyber risks to digital twins and how could these impact industrial elements of new production processes? How do we ensure that data-driven decisions are transparent and auditable? Will the convergence of technology in the wild with traditionally isolated operational technology prove too high-risk for widespread adoption?

This Special Issue aims to provide a forum for the discussion of both opportunity and cyber risk arising from data-driven industrial Internet of Things, with a key focus on the critical factors that will both enable and restrict adoption.

Prof. Dr. Pete Burnap
Guest Editor

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • Internet of Things
  • Smart sensors
  • Cybersecurity
  • Cyber risk
  • Digital twins
  • Manufacturing

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 1542 KiB  
Article
Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a “Chatty Device”
by Mike Lakoju, Nemitari Ajienka, M. Ahmadieh Khanesar, Pete Burnap and David T. Branson
Sensors 2021, 21(15), 4991; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154991 - 22 Jul 2021
Cited by 9 | Viewed by 2360
Abstract
To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of [...] Read more.
To create products that are better fit for purpose, manufacturers require new methods for gaining insights into product experience in the wild at scale. “Chatty Factories” is a concept that explores the transformative potential of placing IoT-enabled data-driven systems at the core of design and manufacturing processes, aligned to the Industry 4.0 paradigm. In this paper, we propose a model that enables new forms of agile engineering product development via “chatty” products. Products relay their “experiences” from the consumer world back to designers and product engineers through the mediation provided by embedded sensors, IoT, and data-driven design tools. Our model aims to identify product “experiences” to support the insights into product use. To this end, we create an experiment to: (i) collect sensor data at 100 Hz sampling rate from a “Chatty device” (device with sensors) for six common everyday activities that drive produce experience: standing, walking, sitting, dropping and picking up of the device, placing the device stationary on a side table, and a vibrating surface; (ii) pre-process and manually label the product use activity data; (iii) compare a total of four Unsupervised Machine Learning models (three classic and the fuzzy C-means algorithm) for product use activity recognition for each unique sensor; and (iv) present and discuss our findings. The empirical results demonstrate the feasibility of applying unsupervised machine learning algorithms for clustering product use activity. The highest obtained F-measure is 0.87, and MCC of 0.84, when the Fuzzy C-means algorithm is applied for clustering, outperforming the other three algorithms applied. Full article
(This article belongs to the Special Issue Cyber Risk in the Industrial Internet of Things)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop