Open Access

ARTICLE

Performance Evaluation of Dynamic Adaptive Routing (DAR) for Unmanned Aerial Vehicle (UAV) Networks

Khadija Slimani^1,2,*, Samira Khoulji², Hamed Taherdoost^3,4, Mohamed Larbi Kerkeb⁵

1 EsieaLab LDR, Higher School of Computer Science, Electronics and Automation (ESIEA), Paris, 75005, France
2 ISI Laboratory, National School of Applied Sciences (ENSA), Abdelmalek Essaadi University, Tetouan, 93000, Morocco
3 School of Arts, Science, and Technology, University Canada West, Vancouver, BC V6B 1V9, Canada
4 GUS Institute, Global University Systems, London, EC1N 2LX, UK
5 Ibn Tofail University, Kenitra, 14000, Morocco

* Corresponding Author: Khadija Slimani. Email:

Computers, Materials & Continua 2025, 85(2), 4115-4132. https://doi.org/10.32604/cmc.2025.066544

Received 10 April 2025; Accepted 21 August 2025; Issue published 23 September 2025

Abstract

Reliable and efficient communication is essential for Unmanned Aerial Vehicle (UAV) networks, especially in dynamic and resource-constrained environments such as disaster management, surveillance, and environmental monitoring. Frequent topology changes, high mobility, and limited energy availability pose significant challenges to maintaining stable and high-performance routing. Traditional routing protocols, such as Ad hoc On-Demand Distance Vector (AODV), Load-Balanced Optimized Predictive Ad hoc Routing (LB-OPAR), and Destination-Sequenced Distance Vector (DSDV), often experience performance degradation under such conditions. To address these limitations, this study evaluates the effectiveness of Dynamic Adaptive Routing (DAR), a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints. The research utilizes the Network Simulator 3 (NS-3) platform to conduct controlled simulations, measuring key performance indicators such as latency, Packet Delivery Ratio (PDR), energy consumption, and throughput. Comparative analysis reveals that DAR consistently outperforms conventional protocols, achieving a 20%–30% reduction in latency, a 25% decrease in energy consumption, and marked improvements in throughput and PDR. These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios. By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments, this study contributes to advancing adaptive routing strategies. The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence–driven decision-making and real-world UAV deployment. Future work will explore cross-layer optimization, multi-UAV coordination, and experimental validation in field trials, aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.

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Keywords

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