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Human Science and Technologies for Defence Networks and Applications Based on Intelligent Algorithms and Smart Sensors

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 6642

Special Issue Editors

Dr. James Kang

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: health Informatics; IoT sensors and networks; Data inferencing system; mHealth; disaster recovery; smart environment; digital health; network security
Special Issues, Collections and Topics in MDPI journals
Dr. Jumana Abu-Khalaf

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: remote health monitoring; wearable sensors; robotics; autonomous vehicles; and computer vision
Dr. Ahmed Ibrahim

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: critical infrastructure; Internet of Things (IoT); cyber radicalisation; cyber security risk
Dr. Mohiuddin Ahmed

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: cybersecurity; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Dr. Naeem Janjua

E-Mail Website
Guest Editor
Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: decision support systems; complex event processing; Internet of Things (IoT); software architecture modelling

Special Issue Information

Dear Colleagues,

In the defence network domain, human performance plays a key role for successful operations and combat. Human factors are deemed to be the most important and critical in defence operations and systems including networks and applications. This Special Issue aims at enhancing the capability of humans, their cognitive and physical performance, survivability, and protection at the individual as well as group levels. This includes studies into:

  • AI (machine learning) to collect, transmit, process and create meaningful and effective data
  • Human sensory, perceptual and cognitive processes
  • Algorithms for situation and decision making
  • Activity Recognition (AR) in line with health data
  • Impact and measurement of stressors on human performance based on wearables and sensor networks
  • Behaviour analysis and wellbeing of individuals
  • Aspects of health training and treatments and/or technologies to enhance individual physical and mental performance
  • Communications with humans and human-machine interfaces.

Dr. James Kang
Dr. Jumana Abu-Khalaf
Dr. Ahmed Ibrahim
Dr. Mohiuddin Ahmed
Dr. Naeem Janjua
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

  • AI, machine learning
  • Inference Algorithms for data efficiency and accuracy for data transmission
  • Personal health device (PHD) sensors and networks
  • Security in IoT and PHD networks
  • Smart sensors
  • Remote sensors and security
  • WBAN sensors and security-embedded devices
  • IoT devices integrated with lightweight security
  • Anomaly detection
  • Access control for sensor networks
  • Actuators and sensors for smart home and smart grid
  • Security architecture for wide area sensor networks, e.g., LPWAN
  • IoT and sensor devices control and management networks
  • Security algorithms for remote sensor networks
  • Block-chain cryptography for sensor networks
  • Security in parallel and distributed sensor networks and systems
  • Security in mobile and wireless communications
  • Activity Recognition
 
 

Published Papers (3 papers)

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Research

12 pages, 1003 KiB  
Article
Nafion Modified Titanium Nitride pH Sensor for Future Biomedical Applications
by Shimrith Paul Shylendra, Magdalena Wajrak, Kamal Alameh and James Jin Kang
Sensors 2023, 23(2), 699; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020699 - 07 Jan 2023
Cited by 6 | Viewed by 2110
Abstract
pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with [...] Read more.
pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with the potential to be used for pH regulation inside the human body and for other biomedical, industrial, and environmental applications. One of the main limitations of existing solid-state pH sensors is their reduced performance in high redox mediums. The potential shift E0 value of the previously developed TiN pH electrode in the presence of oxidizing or reducing agents is 30 mV. To minimize this redox shift, a Nafion-modified TiN electrode was developed, tested, and evaluated in various mediums. The Nafion-modified electrode has been shown to shift the E0 value by only 2 mV, providing increased accuracy in highly redox samples while maintaining acceptable reaction times. Overcoming the redox interference for pH measurement enables several advantages of the Nafion-modified TiN electrode over the standard pH glass electrode, implicating its use in medical diagnosis, real-time health monitoring, and further development of miniaturized smart sensors. Full article
(This article belongs to the Special Issue Human Science and Technologies for Defence Networks and Applications Based on Intelligent Algorithms and Smart Sensors)
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17 pages, 2486 KiB  
Article
Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm
by Angela An, Mohammad Al-Fawa’reh and James Jin Kang
Sensors 2022, 22(24), 9679; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249679 - 10 Dec 2022
Cited by 1 | Viewed by 1464
Abstract
Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables [...] Read more.
Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency. Full article
(This article belongs to the Special Issue Human Science and Technologies for Defence Networks and Applications Based on Intelligent Algorithms and Smart Sensors)
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14 pages, 18640 KiB  
Article
Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model
by Md Mursalin, Mohiuddin Ahmed and Paul Haskell-Dowland
Sensors 2022, 22(22), 8988; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228988 - 20 Nov 2022
Cited by 6 | Viewed by 1667
Abstract
Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work [...] Read more.
Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application. Full article
(This article belongs to the Special Issue Human Science and Technologies for Defence Networks and Applications Based on Intelligent Algorithms and Smart Sensors)
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