UAV Assisted SWIPT Enabled NOMA Based D2D Network for Disaster Management

Ghosh, Sayanti; Roy, Sanjay Dhar; Kundu, Sumit

doi:10.1007/s11277-022-10044-1

Cited by 5 publications

(2 citation statements)

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“…For instance, in highly dense urban areas where several devices coexist within a distance of a few meters, they can benefit through the utilization of a cooperative transmission scheme. Consequently, integrating D2D communications into UAV networks has recently attracted a lot of attention, and related issues have also been studied in the literature [ 19 , 20 , 21 , 22 , 23 ]. In [ 19 ], the authors have derived the closed-form expressions for the outage probability in a UAV-assisted NOMA network with D2D communication capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results confirm the benefits of the proposed D2D-enhanced UAV NOMA network architecture and the efficacy of the planned protocol. The research presented in [ 22 ] proposed a novel approach to address disaster management issues utilizing a UAV-assisted SWIPT-enabled NOMA-based D2D network. They formulated a nonlinear power allocation optimization problem that maximized the system’s energy efficiency performance and solved it using the Dinkelbach approach.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

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The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

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A message verification scheme based on physical layer-enabled data hiding for flying ad hoc network

Mallikarachchi,

Wong,

Lim

2024

Multimed Tools Appl

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The use of Unmanned Aerial Vehicles (UAVs) for military as well as civilian applications such as search and rescue operations, disaster management, parcel delivery and agriculture, is becoming increasingly popular, mainly due to their versatile nature and relatively inexpensive operating costs. However, one of the most significant barriers in the widespread adoption of UAVs for the aforementioned applications is the increasing concern for wireless communication security within the network. Unfortunately, the nodes that comprise these networks are extremely constrained in terms of storage space, processing power and battery life, which require light-weight security protocols that align with these limitations. The current standards for message authentication and verification predominantly rely on lightweight cryptographic techniques. However, there is continual scope for improvement, particularly in the realm of computational efficiency, to better align with the constraints inherent to UAVs. This paper explores data hiding in a multi-UAV environment, to form a light-weight message verification scheme to confirm the identity of the transmitting node. First, five venues having the potential to hide data are analyzed. Based on the analysis of bit error rate obtained from the simulated multi-UAV environment, both cyclic prefix and padding bits are selected. Next, a codeword is generated for each transmission and it is embedded along with the unique ID key, $$\kappa $$ κ of the transmitting node into each venue respectively, and the output is transmitted as Hello packets. Subsequently, the receiving node extracts and decodes the codeword in order to verify the authenticity of the transmitting node. An evaluation of the proposed scheme has been conducted by using a simulated UAV environment. In the best case scenario, the proposed authentication scheme can achieve a successful verification rate of at least 80% at a maximum hop-count of 10 hops. Through computational cost analysis, in comparison to the conventional methods, the proposed scheme was found to have a significantly lower total execution time of $$1.7\mu s$$ 1.7 μ s . In addition, the security analysis shows that when the proposed scheme is paired with physical unclonable functions (PUFs), it is able to resist common security attacks, including man-in-the-middle, replay and cloning attacks.

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Exploiting HDU/FDU-NOMA Schemes for Reliable Communication in Post-disaster Scenario

Kumar,

Srivastava,

Kumar

2024

Wireless Pers Commun

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UAV Assisted SWIPT Enabled NOMA Based D2D Network for Disaster Management

Cited by 5 publications

References 35 publications

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

A message verification scheme based on physical layer-enabled data hiding for flying ad hoc network

Exploiting HDU/FDU-NOMA Schemes for Reliable Communication in Post-disaster Scenario

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