A hybrid approach for cluster head determination of unmanned aerial vehicle in flying ad-hoc networks

Kumar, Kundan; Arya, Rajeev

doi:10.1007/s13198-021-01057-3

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…Additionally, hybrid BIA improves performance with FANET routing compared to non-hybrid BIA. The selection of cluster heads in FANET in HGSOFA [93], a hybrid BIA with GSO and FA, is optimized for energy efficiency. HSSOGSA [94] is the combination of sperm swarm optimization (SSO) and gravitational search algorithm (GSA).…”

Section: Disadvantagementioning

confidence: 99%

“…Simulation Tool Used Metrics Evaluated [116] Cluster building time and lifetime, energy consumption, delivery success probability [113] Average transfer time and cognitive overheads, Network efficiency, Convergence time [93] Cluster building time, energy consumption, First node death analysis [106] MATLAB Cluster building time and lifetime, energy consumption, delivery success probability [49] Energy consumption, Cluster lifetime, Throughput, Packet delivery ratio [91] Positioning and Clustering Performance.…”

Section: Referencementioning

confidence: 99%

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Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method's simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.INDEX TERMS Bio-Inspired Algorithm (BIA), Flying Ad hoc Networks (FANET), Metaheuristic optimization, Systematic Literature Review (SLR), Unmanned Aerial Vehicle (UAV).

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Section: Disadvantagementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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“…7 The present ideas focused on handling several difficulties in individual tasks like work on decreasing the power utilization by estimating the UAV trajectory or developing the bio-inspired routing approaches to enhance the data communication efficacy. 8 However, several techniques fail to underline the entire trouble of assuring effective information transmission in large-scale FANETs, when assuring minimal delay, power utilization, and packet loss. Load balancing is the process of dispersing model traffic flow against divergent servers.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional experiments displayed that these issues result in large power network stability issues and transmission overheads 7 . The present ideas focused on handling several difficulties in individual tasks like work on decreasing the power utilization by estimating the UAV trajectory or developing the bio‐inspired routing approaches to enhance the data communication efficacy 8 . However, several techniques fail to underline the entire trouble of assuring effective information transmission in large‐scale FANETs, when assuring minimal delay, power utilization, and packet loss.…”

Section: Introductionmentioning

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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Improved Beluga Whale Optimizer-Based Cluster Head Selection in FANET

Venkatasubramanian

2023

2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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A hybrid approach for cluster head determination of unmanned aerial vehicle in flying ad-hoc networks

Cited by 4 publications

References 35 publications

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

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

Improved Beluga Whale Optimizer-Based Cluster Head Selection in FANET

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