A Dynamic Clustering Mechanism With Load-Balancing for Flying Ad Hoc Networks

Asaamoning, Godwin; Mendes, Paulo; Magaia, Naércio

doi:10.1109/access.2021.3130417

Cited by 15 publications

(15 citation statements)

References 33 publications

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“…This research proposes a mechanism whose performance is assessed by contrasting it with three topology construction methods, K-means [13] , DCM [14] and MMF [15] .…”

Section: Performance Analysismentioning

confidence: 99%

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

Fang,

Kuang,

Zhang

et al. 2024

International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024)

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The fast development and restricted energy qualities of Unmanned Aerial Vehicles (UAVs) lead to visit changes in the topology of UAV networks, to adapt to this present circumstance, this paper proposes a distributed topology control based on reinforcement learning in unmanned aerial vehicles networks. Neighbor discovery, Election of cluster heads, establishment of clusters, and cluster maintenance are carried out based on parameters such as UAV survival time, connectivity and link stability time. Reinforcement learning method is used to determine the optimal cluster reconfiguration strategy. For every parameter used in CH election, a set of appropriate weights is determined by the maintenance approach to preserve a stable network topology and extend the network lifetime. The strategies are rewarded based on how well they perform on the network. The mechanism proposed in this research was assessed by contrasting it with three topology construction methods, K-means, DCM and MMF. The results show that the method in this paper is applicable to UAV networks with different degrees of improvement in topology maintenance time, packet delivery ratio, end-to-end delay, network throughput, and average node survival time.

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“…This research proposes a mechanism whose performance is assessed by contrasting it with three topology construction methods, K-means [13] , DCM [14] and MMF [15] .…”

Section: Performance Analysismentioning

confidence: 99%

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

Fang,

Kuang,

Zhang

et al. 2024

International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024)

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“…31 Multiple complexities and features presented in the conventional load-balancing techniques adopted for FANET are displayed in Table 1. Political optimizer 18 provides effective dissemination of the data packet with better scalability and wireless reliability factors. It secures better performance related to energy consumption, and it effectively minimizes the transmission delay in the network.…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

Ilango,

Sivarajan,

RaviChandran

et al. 2024

Int J Communication

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SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

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“…Bio-inspired clustering (BIC) based algorithm is proposed in the literature [ 13 ], BIC uses the hybrid gray wolf optimization (HGWO) algorithm for optimization of cluster formation and cluster head selection to reduce energy consumption by considering the number of clusters, cluster size, cluster stability and the number of transmissions. A new dynamic clustering mechanism for load balancing is proposed in the literature [ 14 ], based on Political Optimizer (PO) algorithm for clustering and Shannon entropy function to solve the cluster fault tolerance and traffic overload problems for efficient packet transmission and load balancing.…”

Section: Related Workmentioning

confidence: 99%

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

Yan

Xia

Zhang

et al. 2022

Entropy

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With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.

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A Dynamic Clustering Mechanism With Load-Balancing for Flying Ad Hoc Networks

Cited by 15 publications

References 33 publications

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

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