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Sensors
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Pervasive Intelligence for Sensor and Cyber Information
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Pervasive Intelligence for Sensor and Cyber Information

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 13898

Special Issue Editors

Prof. Dr. Henry Leung

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Guest Editor
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent sensors; machine learning; data analytics; information fusion; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Flavia C. Delicato

E-Mail Website
Guest Editor
Department of Computer Science, Fluminense Federal University, Niteroi 24210-310, RJ, Brazil
Interests: Internet of Things; middleware; edge computing; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fuhua Lin

E-Mail Website
Guest Editor
School of Computing and Information Systems, Athabasca University, Athabasca, AB T9S 3A3, Canada
Interests: adaptive systems; intelligent systems; multiagent systems; virtual reality; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo F. Pires

E-Mail Website
Guest Editor
Department of Computer Science, Fluminense Federal University，Niterói, Rio de Janeiro, Brazil
Interests: Internet of Things; Edge Computing; Software Development of Ubiquitous Systems

Special Issue Information

Dear Colleagues,

Over the last fifty years, computational intelligence has evolved from logic-based artificial intelligence, nature-inspired computing, and social-oriented agent technology to cyber-physical integrated ubiquitous intelligence towards pervasive intelligence (PI). There are several challenges involved in integrating intelligence techniques into cyber-physical systems in order to control intelligent processes and produce insights as well as valuable and actionable information. To address these challenges, there is a need to establish new science and research portfolios that incorporate cyber-physical, cyber-social, cyber-intelligent, and cyber-life technologies in a cohesive and efficient manner. This Special Issue aims to highlight the latest research results and advances focused on how to enable pervasive intelligence in everyday devices to learn and dynamically support human preferences and lifestyles at home, at work, and on the move. We are also interested in how to tackle challenges such as human control, accessibility, safety, and trust associated with the cyberspace. This Special Issue will also contain selected papers from the 19th IEEE International Conference on Pervasive Intelligence and Computing (PICom 2021) and the 6th IEEE Cyber Science and Technology Congress (CyberSciTech 2021).

Prof. Dr. Henry Leung
Prof. Dr. Flavia C. Delicato
Prof. Dr. Fuhua Lin
Prof. Dr. Paulo F. Pires
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. 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

  • pervasive intelligence
  • ubiquitous intelligence
  • cyber-physical computing
  • internet of things
  • smart/intelligent sensors
  • device virtualization
  • edge intelligence

Published Papers (3 papers)

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Research

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18 pages, 9579 KiB  
Article
Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data
by Long Luu, Arvind Pillai, Halsey Lea, Ruben Buendia, Faisal M. Khan and Glynn Dennis
Sensors 2022, 22(11), 3989; https://0-doi-org.brum.beds.ac.uk/10.3390/s22113989 - 24 May 2022
Cited by 11 | Viewed by 3616
Abstract
Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation [...] Read more.
Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96–99%) and personalization (98–99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation. Full article
(This article belongs to the Special Issue Pervasive Intelligence for Sensor and Cyber Information)
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20 pages, 518 KiB  
Article
Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning
by Ameer Ivoghlian, Zoran Salcic and Kevin I-Kai Wang
Sensors 2022, 22(3), 1019; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031019 - 28 Jan 2022
Cited by 5 | Viewed by 1942
Abstract
Wireless networks are trending towards large scale systems, containing thousands of nodes, with multiple co-existing applications. Congestion is an inevitable consequence of this scale and complexity, which leads to inefficient use of the network capacity. This paper proposes an autonomous and adaptive wireless [...] Read more.
Wireless networks are trending towards large scale systems, containing thousands of nodes, with multiple co-existing applications. Congestion is an inevitable consequence of this scale and complexity, which leads to inefficient use of the network capacity. This paper proposes an autonomous and adaptive wireless network management framework, utilising multi-agent deep reinforcement learning, to achieve efficient use of the network. Its novel reward function incorporates application awareness and fairness to address both node and network level objectives. Our experimental results demonstrate the proposed approach’s ability to be optimised for application-specific requirements, while optimising the fairness of the network. The results reveal significant performance benefits in terms of adaptive data rate and an increase in responsiveness compared to a single-agent approach. Some significant qualitative benefits of the multi-agent approach—network size independence, node-led priorities, variable iteration length, and reduced search space—are also presented and discussed. Full article
(This article belongs to the Special Issue Pervasive Intelligence for Sensor and Cyber Information)
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Review

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36 pages, 847 KiB  
Review
A Systematic Literature Review on Distributed Machine Learning in Edge Computing
by Carlos Poncinelli Filho, Elias Marques, Jr., Victor Chang, Leonardo dos Santos, Flavia Bernardini, Paulo F. Pires, Luiz Ochi and Flavia C. Delicato
Sensors 2022, 22(7), 2665; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072665 - 30 Mar 2022
Cited by 28 | Viewed by 7285
Abstract
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be [...] Read more.
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies. Full article
(This article belongs to the Special Issue Pervasive Intelligence for Sensor and Cyber Information)
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