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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks.

Juhi Agrawal1, Muhammad Yeasir Arafat2

  • 1School of Computer Science, University of Petroleum & Energy Studies, Prem Nagar, Dehradun 248007, India.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces HMAO, a hybrid algorithm for flying ad hoc networks (FANETs). HMAO enhances network stability and data delivery by optimizing cluster head selection and routing for unmanned aerial vehicles (UAVs).

Keywords:
FANETsUAV networksaquila optimizerbio-inspired algorithmclusteringmountain gazelle optimizerroutingunmanned aerial vehicles

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) in flying ad hoc networks (FANETs) present unique mobility challenges.
  • Traditional clustering and routing methods struggle with stability, resource efficiency, and latency in dynamic FANET environments.

Purpose of the Study:

  • To develop a novel hybrid bio-inspired algorithm, HMAO, for improved clustering and routing in FANETs.
  • To enhance network stability, data delivery reliability, and resource utilization in dynamic UAV networks.

Main Methods:

  • Proposed HMAO algorithm, combining Mountain Gazelle Optimizer (MGO) for cluster head (CH) selection and Aquila Optimizer (AO) for routing.
  • MGO considers UAV energy, mobility, intra-cluster distance, and neighbor density for stable CH selection.
  • AO utilizes predictive mobility, load balancing, fault tolerance, and ferry nodes for reliable data transmission.

Main Results:

  • HMAO demonstrated superior cluster stability compared to existing methods.
  • Significant improvements observed in packet delivery ratio and reduced network delay.
  • Lower energy consumption and reduced overhead were achieved with the HMAO technique.

Conclusions:

  • The hybrid HMAO algorithm effectively addresses the challenges of clustering and routing in FANETs.
  • HMAO offers a robust solution for stable, efficient, and reliable data communication in dynamic UAV networks.
  • Simulation results validate HMAO's performance advantages over conventional approaches.