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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: closed (30 June 2021).

Special Issue Editor

Dr. Omprakash Kaiwartya
E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK
Interests: internet of vehicles; electric vehicles; IoT use case of sensor networks
Special Issues, Collections and Topics 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 You are welcome to submit an unpublished original research work related to the theme of ‘Physical Layer Security for Sensor Enabled Heterogeneous Networks’.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 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 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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Keywords

  • 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 (3 papers)

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Research

Article
Grouping and Sponsoring Centric Green Coverage Model for Internet of Things
Sensors 2021, 21(12), 3948; https://doi.org/10.3390/s21123948 - 08 Jun 2021
Viewed by 703
Abstract
Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target [...] Read more.
Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network. Full article
(This article belongs to the Special Issue Physical Layer Security for Sensor Enabled Heterogeneous Networks)
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Article
Towards Green Computing Oriented Security: A Lightweight Postquantum Signature for IoE
Sensors 2021, 21(5), 1883; https://doi.org/10.3390/s21051883 - 08 Mar 2021
Cited by 1 | Viewed by 562
Abstract
Postquantum cryptography for elevating security against attacks by quantum computers in the Internet of Everything (IoE) is still in its infancy. Most postquantum based cryptosystems have longer keys and signature sizes and require more computations that span several orders of magnitude in energy [...] Read more.
Postquantum cryptography for elevating security against attacks by quantum computers in the Internet of Everything (IoE) is still in its infancy. Most postquantum based cryptosystems have longer keys and signature sizes and require more computations that span several orders of magnitude in energy consumption and computation time, hence the sizes of the keys and signature are considered as another aspect of security by green design. To address these issues, the security solutions should migrate to the advanced and potent methods for protection against quantum attacks and offer energy efficient and faster cryptocomputations. In this context, a novel security framework Lightweight Postquantum ID-based Signature (LPQS) for secure communication in the IoE environment is presented. The proposed LPQS framework incorporates a supersingular isogeny curve to present a digital signature with small key sizes which is quantum-resistant. To reduce the size of the keys, compressed curves are used and the validation of the signature depends on the commutative property of the curves. The unforgeability of LPQS under an adaptively chosen message attack is proved. Security analysis and the experimental validation of LPQS are performed under a realistic software simulation environment to assess its lightweight performance considering embedded nodes. It is evident that the size of keys and the signature of LPQS is smaller than that of existing signature-based postquantum security techniques for IoE. It is robust in the postquantum environment and efficient in terms of energy and computations. Full article
(This article belongs to the Special Issue Physical Layer Security for Sensor Enabled Heterogeneous Networks)
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Article
Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
Sensors 2020, 20(6), 1730; https://doi.org/10.3390/s20061730 - 20 Mar 2020
Cited by 2 | Viewed by 1192
Abstract
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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