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Smart Sensor Technologies: Transforming Physical Security into the Digital World

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

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 12501

Special Issue Editors


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Guest Editor
School of Computing, Edinburgh Napier University (ENU), Edinburgh, UK
Interests: cybersecurity; machine learning; data analytics; virtualisation and cyber-physical system
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden
2. College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates
Interests: cybersecurity; biometrics; network security; Internet-of-Things security; image analysis

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Guest Editor
School of Engineering and Information Technology (SEIT), University of New South Wales (UNSW)'s UNSW Canberra, Sydney, NSW 2052, Australia
Interests: threat detection; digital forensic

Special Issue Information

Dear Colleagues,

Physical security has been critical to national security and public safety. It ensures the defence and protection of a nation (including its citizens, businesses, and institutions) against threats to their well-being via access control and surveillance. Smart sensing technologies, underpinned by resource-rich sensors and artificial intelligence (AI), connect physical security to the digital world.

The data generated from various sensing devices (such as Infrared Sensors, Motion Sensors, Sound sensors, Radar sensors, Satellite, Optical sensors, Ultrasonic sensors, Chemical sensors, Magnetic sensors, etc.) and other sources (such as video footage, audio records, access control logs, social media, police records, etc.) can be correlated and analysed by AI systems to drive the growing sophistication of physical security and build a safer nation. Besides, packing sensors with AI could be used to minimise the threat of malicious activities to digital assessts.

This Special Issue is addressed to applications of smart sensor technologies for all types of physical security.

  • Application of sensing technologies in national border security
  • Application of sensing technologies in public safety, including road traffic monitoring, crowd tracking, crime prevention, etc.
  • Application of sensing technologies in industrial security, including access control, intrustion detection, safe manufacturing, etc.
  • Autonomous sensor-assisted physical security systems, such as autonomous security robots, natural disaster management using multi-sensor UAV, etc.
  • AI-driven sensed data correlation and analysis for phyaical security
  • Surveillance data analytics using machine learning
  • Verifiable sensor-based physical security
  • Digital twins for physical security

Dr. Zhiyuan Tan
Dr. Ali Ismail Awad
Dr. Nour Moustafa
Guest Editors

Manuscript Submission Information

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Published Papers (3 papers)

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Research

18 pages, 1482 KiB  
Article
Microservice Security Framework for IoT by Mimic Defense Mechanism
by Fei Ying, Shengjie Zhao and Hao Deng
Sensors 2022, 22(6), 2418; https://doi.org/10.3390/s22062418 - 21 Mar 2022
Cited by 5 | Viewed by 3008
Abstract
Containers and microservices have become the most popular method for hosting IoT applications in cloud servers. However, one major security issue of this method is that if a container image contains software with security vulnerabilities, the associated microservices also become vulnerable at run-time. [...] Read more.
Containers and microservices have become the most popular method for hosting IoT applications in cloud servers. However, one major security issue of this method is that if a container image contains software with security vulnerabilities, the associated microservices also become vulnerable at run-time. Existing works attempted to reduce this risk with vulnerability-scanning tools. They, however, demand an up-to-date database and may not work with unpublished vulnerabilities. In this paper, we propose a novel system to strengthen container security from unknown attack using the mimic defense framework. Specifically, we constructed a resource pool with variant images and observe the inconsistency in execution results, from which we can identify potential vulnerabilities. To avoid continuous attack, we created a graph-based scheduling strategy to maximize the randomness and heterogeneity of the images used to replace the current images. We implemented a prototype using Kubernetes. Experimental results show that our framework makes hackers have to send 54.9% more random requests to complete the attack and increases the defence success rate by around 8.16% over the baseline framework to avoid the continuous unknown attacks. Full article
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28 pages, 15494 KiB  
Article
Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras
by Pedro Teixidó, Juan Antonio Gómez-Galán, Rafael Caballero, Francisco J. Pérez-Grau, José M. Hinojo-Montero, Fernando Muñoz-Chavero and Juan Aponte
Sensors 2021, 21(21), 7379; https://doi.org/10.3390/s21217379 - 6 Nov 2021
Cited by 5 | Viewed by 5443
Abstract
Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber [...] Read more.
Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack. Full article
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17 pages, 2911 KiB  
Article
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
by Jialuan He, Zirui Xing, Tianqi Xiang, Xin Zhang, Yinghai Zhou, Chuanyu Xi and Hai Lu
Sensors 2021, 21(17), 5688; https://doi.org/10.3390/s21175688 - 24 Aug 2021
Cited by 1 | Viewed by 1780
Abstract
In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation [...] Read more.
In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction. Full article
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