Design Principles for Cooperative Relaying on UAVs-based FANET

Bashir, Musavir; Iqbal, Sameera; Yusof, Kamaludin Mohamad

doi:10.1109/aset53988.2022.9735058

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…Additionally, ad hoc networking among UAVs solves problems that arise in single UAV systems, such as short range, network failure, and limited guidance [65]. FANET can be used to achieve high-level goals and to support several applications, such as improved routing protocols and mobility [66], cooperative communication and diversity [67], and maximizing throughput on NOMA-assisted routing [68]. Likewise, in [69], a swarm of UAVs was used to improve wireless connectivity in NOMA systems, leading to high resource efficiency.…”

Section: F Fanet-based Uavmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. In these networks, to support more UE, multiple UE share the same frequency resources, which can be beneficial for UAV communication networks. To achieve optimal results, UAVs need to adaptively adjust to their environment, and this can be achieved through employing machine learning (ML). ML is a subfield of artificial intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Employing ML can aid NOMA for UAV networks to analyze various parameters and rely on accurate predictions regarding various aspects of communication, such as channel conditions, user mobility, and traffic demands. The use of ML in NOMA-UAV networks can lead to significant improvements in wireless communication systems. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first be introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and applications of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research.INDEX TERMS Aerial networks, machine learning (ML), non-orthogonal multiple Access (NOMA), unmanned aerial vehicles (UAV).

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Section: F Fanet-based Uavmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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“…UAVs with high-resolution cameras and sensors capture real-time data from disaster-stricken areas, providing information about damage, hazards, and locations of victims in need, enabling prompt response efforts [3]. UAVs also serve as communication relays facilitating information exchange between responders and victims and can deliver essential supplies to inaccessible areas [4] [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Connectivity and collision constrained opportunistic routing for emergency communication using UAV

Das

Bapat

et al. 2023

Computer Networks

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“…On these conditions, UAV routing protocols and communication relaying principles stand at significant points [6][7] [8]. On the other ways, UAVs get frequent security challenges and failures due to specifically initiated attacks into the network.…”

Section: Introductionmentioning

confidence: 99%

Reactive Handover Coordination System with Regenerative Blockchain Principles for Swarm Unmanned Aerial Vehicles

Rajasoundaran

Kumar

Selvi

et al. 2023

Preprint

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Aerial warfare systems in various countries highly expect Swarm Unmanned Aerial Vehicles (UAVs) and Flying Adhoc Networks (FANETs) for defense stability. However, swarm UAVs face many challenges, like security breaches, malfunctions, link failures, and handover failures. UAVs can easily malfunction by external threats to create data loss, theft, signal jamming, misrouting, false handover, and location spoofing. Failures in UAV handover principles make overall gradual downtime in FANET. Against these issues, existing UAV protection mechanisms deliver location monitoring mechanisms (graphs and trees), multi-link handover mechanisms, and distributed authentication principles. Anyhow, the implantation of recent-day techniques may fail against migrating attacker events executed in electronic warfare systems. The methods need improvements in protecting Multi-UAV layers through end-to-end security principles. On the research problem, the proposed Reactive Handover Coordination System with Regenerative Blockchain Principles (RHCRB) takes novel high-security features specially made for swarm UAVs. RHCRB implements more crucial distributed functions in each UAV on demand. The executed operations of RHCRB comprise trusted location monitoring schemes (internal and cooperative UAV movements), dynamic location-based cost magnitude calculations, regenerative blockchain principles (authentication of each UAV and active edges), confidential link management principles, secure handover coordination, and on-demand topology management principles. The technical aspects of RHCRB build lightweight and shielded handover principles against airfield vulnerabilities. The proposed model suggests implementing an entirely protected handover environment from node localization to handover events. The inspired technical aspects of RHCRB contribute to the swarm UAV environment through confidential (Advanced Encryption Standard (AES)) and distributed authentication (blockchain-based node and edge management) principles to engage protected handover practices. The experimental section of this article has the testbed in FlyNetSim tool for implementing RHCRB and notable recent security techniques such as the Internet of Vehicles with Decentralized Blockchains (IoV-DB), Group Handover for Internet of Defense (GH-IoD), and Handover and Optimized Security Principles for UAVs (HOOPOE). The results show RHCRB's 8–14% of betterment than existing techniques through various measures.

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Design Principles for Cooperative Relaying on UAVs-based FANET

Cited by 11 publications

References 28 publications

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Connectivity and collision constrained opportunistic routing for emergency communication using UAV

Reactive Handover Coordination System with Regenerative Blockchain Principles for Swarm Unmanned Aerial Vehicles

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