Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network

Cumino, Pedro; Maciel, Kaled; Tavares, Thais; Oliveira, Helder; Rosário, Denis; Cerqueira, Eduardo

doi:10.3390/s20010067

Cited by 15 publications

(9 citation statements)

References 37 publications

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“…Clustering is a technique that is based on the centroid estimate approach used in centroid learning. A continuous search is undertaken in the centroid of the local region until a good result is obtained [59], [60].…”

Section: ) Heuristic Basedmentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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Flying Ad-Hoc Network (FANET) is a set of Unmanned Aerial Vehicles (UAVs) interconnected wirelessly. FANETs self-organize and provide low-cost, adaptable, and simple-to-implement flying nodes, enabling them to complete complicated tasks more quickly and collectively. The high mobility of nodes and the highly dynamic topology pose challenges to communication design, particularly when creating a routing protocol for UAV networks; this has inspired researchers to contribute and develop this technology. Hierarchical routing technique known as clustering is necessary to offer scalability, survivability, and distribute payload among UAVs to maintain the performance. This study has proposed a comprehensive survey of the cluster-based routing protocols (CBRPs) in terms of their strengths, weaknesses, specific applications, method, number of nodes, and future improvements for serving FANETs. Moreover, 21 CBRPs based FANETs were reviewed in terms of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features, cluster head (CH) selection, routing metrics, and performance measures.In addition, open issues that need to be addressed in future studies in the field of routing protocols for UAV networks were also debated. INDEX TERMSClustering algorithm, flying ad hoc network, routing protocol, scalability, unmanned aerial vehicle.

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Section: ) Heuristic Basedmentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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“…A comprehensive solution aiming to achieve low overhead and high performance control message dissemination in an SDN-based FANET is described in [47]. The proposed network architecture is cluster-based with the clustering algorithm being executed at the global controller.…”

Section: B In-band Control Planementioning

confidence: 99%

Software-Defined Networking Meets Software-Defined Radio in Mobile ad hoc Networks: State of the Art and Future Directions

et al. 2022

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The aims of this survey article are to elaborate on cross-layer optimization, Software-Defined Networking (SDN) and Software-Defined Radio (SDR) as separate domains of wireless network design for which a unified view has not been adequately considered to date and present lessons learned, with a view towards the challenges associated with SDN-SDR interaction that would facilitate benefits in crosslayer optimization of mobile ad hoc networks (MANETs). We focus on MANETs because (i) they are still at the forefront of technology, and in some scenarios they are the only meaningful option for establishing communication; (ii) they expose the full potential and benefits of coexistence and interaction of SDN and SDR, in terms of optimizing key performance indicators. While SDN and SDR are mature technologies, their interaction and joint consideration have been largely overlooked. Current SDN approaches do not span the physical (PHY) and medium-access control (MAC) layers, but they rather concentrate on network-level routing and traffic flow optimization. As a result, PHY-and MAC-layer related parameters which notoriously affect key network performance metrics remain static or at best are adapted based on some heuristic or local approaches. On the other hand, the reach of SDR architectures is restricted to the PHY and MAC layers. We discuss the state of the art of cross-layer optimization, SDN and SDR, and current challenges associated with coexistence and interaction of SDN and SDR. Such an interaction would extend the span of SDN to PHY and MAC layers and lead to realizations of centralized approaches across all layers so as to control and optimize parameters, towards global network objectives. It would also create a bridge between centralized network control that is inherent in SDN and the distributed nature of MANETs, with the add-on features of flexible and fast PHY and MAC layer adaptation offered by SDR, for solid, autonomous and ultimately better network control implementations that span all layers, towards realizing and implementing the holy grail of real cross-layer optimization.INDEX TERMS Software-defined networking (SDN), software-defined radio (SDR), coexistence, mobile ad hoc networks (MANETs), cross-layer design and optimization.

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“…All drones are connected to another drone that is set as configuration parameter during the deployment of the system or selected during operation based on the status of the swarm. Such drone is selected to operate as head of a cluster of neighbor drones, meaning that every communication generated or terminated in such neighbors are sent via the cluster head [ 34 ]. In this situation, the cluster head may become a bottleneck within the drone swarm causing link blockage and high latency.…”

Section: Drone Swarms As Networked Control Systemsmentioning

confidence: 99%

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

Asaamoning

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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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Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network

Cited by 15 publications

References 37 publications

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Software-Defined Networking Meets Software-Defined Radio in Mobile ad hoc Networks: State of the Art and Future Directions

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

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