Open Access

ARTICLE

Digital Twin-Driven Intelligent Routing for UAV-Assisted Smart Mobility and Disaster-Aware FANETs

Jasmine Batra¹, Kiranbir Kaur¹, Fuad Ali Mohammed Al-Yarimi², Abdulrahman Mohammed Alamoudi³, Salil Bharany⁴, Ateeq Ur Rehman^5,*, Jaeyoung Choi^5,*

1 Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, India
2 Applied College of Mahail Aseer, King Khalid University, Muhayil Aseer, Saudi Arabia
3 Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia
4 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
5 School of Computing, Gachon University, Seongnam-si, Republic of Korea

* Corresponding Authors: Ateeq Ur Rehman. Email: ; Jaeyoung Choi. Email:

(This article belongs to the Special Issue: Digital Twin-Enabled Intelligent Transportation Systems: Computational Modeling, AI Integration, and Smart Mobility Applications)

Computer Modeling in Engineering & Sciences 2026, 148(3), 30 https://doi.org/10.32604/cmes.2026.086048

Received 23 May 2026; Accepted 10 August 2026; Issue published 28 September 2026

Abstract

Flying Ad Hoc Networks (FANETs) are emerging as a key enabler for intelligent transportation systems, smart aerial mobility, disaster response, surveillance, and environmental monitoring. However, their highly dynamic topology, rapid node mobility, intermittent connectivity, and limited energy resources pose major challenges for reliable routing. Existing routing protocols largely depend on instantaneous network information and lack predictive intelligence, leading to unstable links, increased overhead, and degraded performance in dynamic environments. To address these issues, this study proposes a Digital Twin-driven Trust-Aware PSO-based routing framework (DT-TAPSO) for UAV-assisted smart mobility and disaster-aware FANETs. The framework employs a lightweight Digital Twin-inspired predictive synchronization model to forecast future node positions, link quality, mobility patterns, and residual energy, enabling proactive, stability-aware routing decisions. An intelligent decision-making layer integrating fuzzy logic and trust evaluation is incorporated to handle uncertainty and assess node reliability. Additionally, a utility-driven cluster head selection mechanism based on predicted network conditions ensures stable clustering, supported by an adaptive re-clustering strategy to maintain network efficiency. Furthermore, Particle Swarm Optimization (PSO) with link-lifetime awareness is applied for optimal route selection. An edge-assisted disaster-aware communication model is also introduced to reduce latency and enhance responsiveness in mission-critical scenarios. The proposed DT-TAPSO framework is evaluated through simulations against benchmark protocols, including LEACH, HIROL, MCQOR, QLSR-LCN, and QLME. Results demonstrate that DT-TAPSO significantly improves packet delivery ratio, network lifetime, throughput, and energy efficiency, while reducing delay, routing overhead, and hop count. Overall, the framework enables predictive, reliable, and low-latency communication, supporting next-generation intelligent transportation and smart mobility systems.

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Keywords

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