Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols

Chen, Xi; Tang, Jun; Lao, Songyang

doi:10.3390/app10103661

Cited by 109 publications

(60 citation statements)

References 80 publications

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“…This deployment strategy brings reduced downlink bandwidth and latency requirements because of shorter links among drones. The advantages of an ad hoc-based network deployment strategy include network scale-up, fault tolerance, device autonomy, flexible and less expensive to setup [ 6 ].…”

Section: Drone Swarms As Networked Control Systemsmentioning

confidence: 99%

“…In this context, literature already provides several studies about the different families of routing protocols suitable for drone effective communications. These protocols include Swam Intelligence-based protocols, Position-based routing protocols and Topology-based routing protocols [ 6 ].…”

Section: Drone Swarms As Networked Control Systemsmentioning

confidence: 99%

“…To mitigate such limitations, several drones could collaborate with each other in a drone swarm to perform tasks more efficiently while providing coverage for large geographic areas [ 5 ]. This aim to develop swarms of drones have been tackled in projects such as the Low-cost UAV Swarm Technology project [ 6 ], demonstrated at the Farnborough air show in 2016, and the swarm technology introduced by the Chinese Electronics Technology Group [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Asaamoning

Mendes

Rosário

et al. 2021

Sensors

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The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

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Section: Drone Swarms As Networked Control Systemsmentioning

confidence: 99%

Section: Drone Swarms As Networked Control Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Asaamoning

Mendes

Rosário

et al. 2021

Sensors

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“…However, as mentioned previously, UAVs have several purposes, and somehow, different challenges arise when considering a communication network from when aiming to connect the UAVs among themselves. XiJun et al [28] focused on UAV swarm communication architectures and routing protocols. They detailed four communication architectures for UAV swarms.…”

Section: A Surveys On Uav Flocks Creation Management and Coordinationmentioning

confidence: 99%

Machine Learning Methods for UAV Flocks Management-A Survey

Azoulay¹,

Haddad

Reches

2021

IEEE Access

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The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.

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“…The area of flying things is still evolving, so in the emergence of UAV-networks base-line routing techniques was used. Later on, researchers found that in drone-assisted-flying-networks rapid mobility pattern changes due to those existing routing schemes must be improved [24]. The idea of ad-hoc-wireless-multi-hop-networks combines together to make an efficient way of path planning to overcome on different issues faced by routing mechanisms.…”

Section: Communication Protocols For Ioftmentioning

confidence: 99%

Securing Against DoS/DDoS Attacks in Internet of Flying Things using Experience-based Deep Learning Algorithm

Khan¹,

Abdollahi

Khan

et al. 2021

Preprint

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Due to the limited computational resources of small unmanned aerial vehicles (UAVs), the Internet of flying things (IoFT) is vulnerable to cybersecurity attacks, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. In addition, the transfer of reliable information from source UAV to destination UAV is another big challenge in IoFT networks. Therefore, this article aims to address the security deficiency by proposing an experience-based deep learning algorithm to cater to the DoS, D-DoS and a special kind of threat covering ping-of-death attacks. The proposed scheme uses the notion of the intrusion detection system (IDS). In addition, for reliable communication, a nature-based control routing algorithm AntHocNet is investigated with other contemporary protocols. The proposed approach is implemented in a smart city environment as a case study. The result authenticates the superiority of the proposed schemes in terms of security and QoS requirement from its counterparts.

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Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols

Cited by 109 publications

References 80 publications

Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Machine Learning Methods for UAV Flocks Management-A Survey

Securing Against DoS/DDoS Attacks in Internet of Flying Things using Experience-based Deep Learning Algorithm

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