BICSF: Bio-Inspired Clustering Scheme for FANETs

Khan, Asif; Aftab, Farooq; Zhang, Zhongshan

doi:10.1109/access.2019.2902940

Cited by 89 publications

(84 citation statements)

References 34 publications

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“…Due to the limitation of drone battery resources and maneuverability, the routing in FANET is unstable. FIGURE 5 shows a biologically-inspired FANETs clustering algorithm, through the use of gray wolf optimization and clustering algorithm based on ant colony optimization, the performance of the system is evaluated in a terms of cluster construction time, energy consumption, cluster life cycle and delivery success probability [35].…”

Section: Figure 4 Three Phases Of Mav Uav Task Coalition Formation B)mentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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Section: Figure 4 Three Phases Of Mav Uav Task Coalition Formation B)mentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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“…Three PSO-based swarm mobility algorithms are proposed in [10] for UAVs performing reconnaissance missions over targets in hostile environments. Other distributed bio-inspired techniques for swarm creation and management are proposed -among others-by [22] and [23]. Glowworm Swarm Optimization (GSO) is used in [22] to cluster the UAVs on the basis of luciferin levels and of their residual energy.…”

Section: Related Workmentioning

confidence: 99%

“…Other distributed bio-inspired techniques for swarm creation and management are proposed -among others-by [22] and [23]. Glowworm Swarm Optimization (GSO) is used in [22] to cluster the UAVs on the basis of luciferin levels and of their residual energy. The cluster-breathing mechanism in [23] allows an UAV swarm to be connected, by using the RSS wireless signal as metric for the steering behaviour.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm keeps track of the the best local candidate solution (visited by particle pa loc w ) and also of the best global candidate solution (visited by particle pa glob ). The space exploration is performed within the main loop which is executed for a number of ite PSO iterations (lines [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. At each iteration, the particle's position and velocity are updated (lines [16][17][18][19][20][21][22] and then, the best and local candidates are also updated in line 23 and line 26, respectively.…”

Section: Algorithm 4 the Pso Optimization Algorithmmentioning

confidence: 99%

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A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

et al. 2020

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In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose ELAPSE (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. ELAPSE integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the ELAPSE framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.

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“…Ali et al [128] propose an adaptive routing scheme that minimizes the issue of unstable routes caused by limited battery and high mobility of UAVs. The proposed scheme uses the glowworm swarm optimization algorithm in an energy-aware cluster formation, which includes CH election.…”

Section: Enhancing Routing Performance Using Swarm-based Clusteringmentioning

confidence: 99%

Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

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BICSF: Bio-Inspired Clustering Scheme for FANETs

Cited by 89 publications

References 34 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

Routing Schemes in FANETs: A Survey

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