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Special Issue "Physical Layer Security for Sensor Enabled Heterogeneous Networks"

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

Deadline for manuscript submissions: 1 October 2020.

Special Issue Editor

Dr. Omprakash Kaiwartya
Guest Editor
School of Science and Technology, Nottingham Trent University, Clifton Campus, NG11 8NS, UK
Interests: Internet of Connected Vehicles (IoV), IoT use case Implementation of Sensor Networks, Electric Vehicle Charging Management: Recommendation and Planning (EV)
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleague,

The connected environment is enlarging rapidly due to the sensor-enabled growing smartness in devices or things around us in our daily life. The growing connectivity via computing- and communication-enabled smart devices is also increasing the number of heterogeneous wireless technologies around us, resulting in greater security challenges. The traditional cryptography-centric security techniques are becoming nearly impractical for these very small and smart sensor-enabled devices due to the high volume of computation requirement. In today’s growing connected world scenario, physical layer security is one of the potential solutions for sensor-enabled heterogeneous networks. The physical layer security focuses on signal level computation, identification, diversion, integration, and data analytics for secure localized centric communication. Signal level operating techniques such as beamforming, simultaneous wireless information and power transfer (SWIPT), multiple input and multiple output (MIMO), etc. have become highly potential research themes in today’s dense and heterogeneous wireless networking uses cases. You are welcome to submit an unpublished original research work related to the theme of ‘Physical Layer Security for Sensor Enabled Heterogeneous Networks’.

Dr. Omprakash Kaiwartya
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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 papers will be 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.

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  • Physical level security frameworks for sensor-enabled heterogeneous networks
  • Optimizing secrecy capacity in sensor-enabled heterogeneous networks
  • Location-centric security frameworks for enabling smart services in heterogeneous networks
  • Light-weight security architectures for next-generation heterogeneous networks
  • Location verification in mobility-centric heterogeneous network environments
  • Beamforming-centric security models for ultra-dense heterogeneous network environments
  • Multiple input multiple output (MIMO)-based security models for heterogeneous networks
  • Simultaneous wireless information and power (SWIPT)-enabled network security models
  • Edge computing-enabled security architecture for mobility-centric heterogeneous networks
  • Interference-aware security models for highly dense heterogeneous networks

Published Papers (1 paper)

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Open AccessArticle
Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
Sensors 2020, 20(6), 1730; - 20 Mar 2020
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO [...] Read more.
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm. Full article
(This article belongs to the Special Issue Physical Layer Security for Sensor Enabled Heterogeneous Networks)
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