Reinforcement Learning-Based Multidimensional Perception and Energy Awareness Optimized Link State Routing for Flying Ad-Hoc Networks

Prakash, M.; Neelakandan, S.; Kim, Bong-Hyun

doi:10.1007/s11036-023-02255-y

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…The node values such as 50, 100, 150, and 200 were taken into consideration. Multiple conventional optimization algorithms such as “Deer Hunting Optimization Algorithm, 36 Butterfly Optimization Algorithm, 37 CSA, 34 SHO, 35 Packet Arrival Prediction (PAP), 27 MPEAOLSR, 29 and Eagle Optimized Energy Efficient Optimal Route‐Finding Protocol (EOEEORFP) 30 ” were employed for the validation of the recommended multi‐constraint load balancing and routing approach.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…In 2023, Prakash et al 29 implemented a Multidimensional Perception and Energy Awareness Optimized Link State Routing (MPEAOLSR) for FANET 30 . The accuracy rate of the recommended approach was better than the other baseline approaches.…”

Section: Existing Workmentioning

confidence: 99%