Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Flying ad-hoc networks (FANETs) face challenges with limited battery energy and high mobility of unmanned aerial vehicles (UAVs).
• These limitations result in short flight times and inefficient routing protocols, hindering practical applications.
• Existing artificial intelligence (AI) techniques like Ant Colony Optimization and Grey Wolf Optimization have been applied but require further optimization.

Purpose of the Study:

• To address the primary challenges of limited battery energy and inefficient routing in FANETs.
• To propose an efficient clustering model that enhances both energy efficiency and network lifetime.
• To improve the performance of FANETs by optimizing transmission power and cluster head selection.

Main Methods:

• Adjusting UAV transmission power based on anticipated operational requirements to minimize packet loss ratio (PLR) and enhance link quality.
• Employing a variant of the K-Means Density clustering algorithm for optimal cluster head selection.
• Evaluating the proposed model against state-of-the-art AI clustering algorithms.

Main Results:

• The proposed clustering model significantly improves energy consumption and extends cluster lifetime compared to existing methods.
• Optimal transmission power adjustment leads to reduced packet loss and better communication link quality.
• K-Means Density clustering effectively enhances cluster head selection, reducing routing overhead.

Conclusions:

• The developed clustering approach offers a viable solution for improving energy efficiency and routing in FANETs.
• This model demonstrates superior performance over Ant Colony Optimization and Grey Wolf Optimization-based clustering algorithms.
• The findings suggest a pathway for more robust and sustainable UAV-based network operations.