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Machine Learning in Wireless Sensor Networks and Internet of Things
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Machine Learning in Wireless Sensor Networks and Internet of Things

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 35027

Special Issue Editor

Prof. Dr. Paul Krause

E-Mail Website
Guest Editor
Computer Science Research Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: machine learning; good old fashioned AI; ecological modelling; medical AI; AI and environmental modelling/monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The combination of Wireless Sensor Networks and Internet of Things (IoT) has the potential to bring “intelligent sensing” into a broad spectrum of applications. We might even consider an ambition of connecting “everything, everywhere,” but certainly there are challenging application domains in agroecology; audience of the future; connected and autonomous vehicles; environmental monitoring; health and wellbeing; industry IV; smart utility supplies; supply chain management; urban living. We have the connectivity, but the next step is to mature the integration of federated machine learning into that infrastructure. This Special Issue will cover the full stack of the integration of machine learning into Wireless Sensor Networks (both above and below ground) and IoT; from foundational research on federated machine learning, through to innovative applications in societally important areas. Papers that show how we can use this technology to help the world achieve its sustainable development goals will be particularly welcomed.

Prof. Dr. Paul Krause
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

  • Smart/Intelligent Sensors
  • Sensor Networks
  • Signal processing, data fusion and deep learning in sensor systems
  • Machine/deep learning and artificial intelligence in sensing and imaging
  • Sensor technology and application
  • Sensor arrays
  • Federated Machine Learning
  • Privacy and security of federated data
  • Mining network structures
  • Adaptive networks

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Published Papers (5 papers)

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Research

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17 pages, 2475 KiB  
Article
Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions
by Zunming Chen, Hongyan Cui, Ensen Wu and Xi Yu
Sensors 2022, 22(2), 684; https://doi.org/10.3390/s22020684 - 17 Jan 2022
Cited by 15 | Viewed by 3145
Abstract
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous [...] Read more.
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)
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16 pages, 775 KiB  
Article
Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul
by Yunji Yang, Yonggi Hong and Jaehyun Park
Sensors 2021, 21(20), 6791; https://doi.org/10.3390/s21206791 - 13 Oct 2021
Cited by 4 | Viewed by 1697
Abstract
In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it [...] Read more.
In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)
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15 pages, 2029 KiB  
Article
A Vision-Based Social Distancing and Critical Density Detection System for COVID-19
by Dongfang Yang, Ekim Yurtsever, Vishnu Renganathan, Keith A. Redmill and Ümit Özgüner
Sensors 2021, 21(13), 4608; https://doi.org/10.3390/s21134608 - 5 Jul 2021
Cited by 73 | Viewed by 10020
Abstract
Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow [...] Read more.
Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)
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Review

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23 pages, 1749 KiB  
Review
A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques
by Daniele Atzeni, Davide Bacciu, Daniele Mazzei and Giuseppe Prencipe
Sensors 2022, 22(13), 4925; https://doi.org/10.3390/s22134925 - 29 Jun 2022
Cited by 11 | Viewed by 6108
Abstract
Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of [...] Read more.
Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)
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35 pages, 4315 KiB  
Review
Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues
by Rami Ahmad, Raniyah Wazirali and Tarik Abu-Ain
Sensors 2022, 22(13), 4730; https://doi.org/10.3390/s22134730 - 23 Jun 2022
Cited by 89 | Viewed by 12431
Abstract
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in [...] Read more.
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks and Internet of Things)
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