Hybrid meta-heuristic routing framework for energy consumption and execution-time optimization in UAV-based FANET networks (FMORT, 2024).
https://doi.org/10.1016/j.comnet.2024.110869
Published in: Computer Networks (Elsevier)
Unmanned Aerial Vehicles (UAVs) operate under strict constraints:
- Limited onboard energy
- High mobility in 3D space
- Rapid topology changes
- Real-time routing requirements
- Network instability risk
Traditional MANET and VANET routing protocols fail under aerial mobility dynamics.
FMORT introduces a hybrid meta-heuristic routing framework for Flying Ad Hoc Networks (FANETs) that simultaneously optimizes:
• Energy Consumption
• Real Execution Time
• Network Lifetime
• Packet Delivery Ratio (PDR)
• End-to-End Delay
• Re-clustering Overhead
FANET routing is significantly more complex than terrestrial ad-hoc networks because:
- UAV nodes move in 3D with high velocity
- Connectivity is highly dynamic
- Frequent re-clustering increases overhead
- Energy depletion leads to network fragmentation
Existing methods often optimize only one objective (e.g., delay or energy).
FMORT performs multi-objective hybrid optimization.
FMORT integrates two swarm-intelligence algorithms:
These operate simultaneously for:
- Intelligent cluster-head selection
- Energy-balanced routing path determination
- Mobility-aware re-clustering
- Redundancy reduction
The framework introduces:
Dynamic thresholding based on:
- Node energy
- Sensitivity rate
- Network density
- Mobility state
Prevents overloading and underloading.
Cluster heads are selected using:
- Average Euclidean distance
- Node connectivity
- Residual energy
- Sensitivity classification
This reduces unnecessary packet retransmissions.
FMORT integrates upper and lower vision ranges in aerial networking, balancing:
- Coverage stability
- Isolation risk
- Energy expenditure
Simulation Tools:
- OPNET
- MATLAB
Network Scale:
40 – 160 UAV nodes
Metrics Evaluated:
- Energy Consumption
- Network Lifetime
- Transmission Delay
- Packet Delivery Ratio
- Re-cluster Lifetime
- Routing Overhead
Compared to MWCRSF and other benchmark algorithms:
• 0.73% reduction in energy consumption
• 2.23% increase in network lifetime
• 1.35% reduction in re-cluster construction time
• 0.11% improvement in re-cluster lifetime
Performance becomes more stable under:
- High mobility
- Large swarm density
- Dynamic workload transmission
Energy reduction in UAV swarms directly impacts:
- Mission duration
- Recharge cycles
- Carbon footprint
- Operational cost
FMORT improves aerial swarm endurance and routing stability.
- Node classification via sensitivity rate
- Hybrid Sparrow–Dragonfly optimization
- Cluster head determination
- Threshold-based mobility regulation
- Dynamic re-clustering
- Load-balanced routing
- Military UAV swarm coordination
- Disaster management
- Smart agriculture monitoring
- Surveillance systems
- Search & rescue missions
- Remote sensing networks
Title: FMORT: The Meta-Heuristic Routing Method by Integrating Index Parameters to Optimize Energy Consumption and Real Execution Time Using FANET
Authors:
Arash Ghorbannia Delavar
Zahra Jormand
Journal: Computer Networks
Year: 2024
DOI: 10.1016/j.comnet.2024.110869
Ghorbannia Delavar, A., & Jormand, Z. (2024). FMORT: The meta-heuristic routing method by integrating index parameters to optimize energy consumption and real execution time using FANET. Computer Networks. https://doi.org/10.1016/j.comnet.2024.110869
@article{FMORT2024, title={FMORT: The Meta-Heuristic Routing Method by Integrating Index Parameters to Optimize Energy Consumption and Real Execution Time Using FANET}, author={Ghorbannia Delavar, Arash and Jormand, Zahra}, journal={Computer Networks}, year={2024}, doi={10.1016/j.comnet.2024.110869} }
Department of Computer Science
Payame Noor University, Tehran, Iran
This repository is created for academic indexing, discoverability, and research visibility purposes.
All publication rights belong to the journal publisher.
This repository is part of a structured research line focused on:
• Energy-Aware Networking
• Hybrid Meta-Heuristic Optimization
• Sensitivity-Driven Resource Allocation
• Load Balancing in Dynamic Systems
• Sustainable Distributed Networks