6G Edge Networks Integrated Intelligent Graph based Learning Model for Heterogeneous Clustered Hybrid UAV in Wireless Powered IoT Communication
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 2 |
| Year of Publication : 2026 |
| Authors : G. Ramkumar |
10.14445/23488549/IJECE-V13I2P109
How to Cite?
G. Ramkumar, "6G Edge Networks Integrated Intelligent Graph based Learning Model for Heterogeneous Clustered Hybrid UAV in Wireless Powered IoT Communication," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 2, pp. 115-132, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P109
Abstract:
The Hybrid Unmanned Aerial Vehicles (UAVs) are getting significant ground in their role as a source of information exchange. This article presents a new network architecture of hybrid UAVs incorporating edge based federated learning, spectrum sensing, and graph based learning models referred to as Intelligent Learning of Heterogeneous Clustered Hybrid UAV (ILHCHU) to enhance information sharing and throughput. The proposed system will use UAVs as the supplementary computing platforms and communication stations, which will provide an aerial view of the environment and help to detect possible road hazards. The architecture includes a K means Clustering Algorithm to enhance resource distribution and a probabilistic model checking to improve battery efficiency. An Intelligent Graph Learning Model (IGLM) is used to collect observation data from adjacent agents, targets, and obstacles. The throughput of the system is evaluated using a saturation throughput model, accounting for packet loss, opportunity loss, and missed detections. Simulation demonstration illustrates that ILHCHU improves information sharing and throughput within a 6G edge network. The results of the simulation are carried out with the following parameter matrices: communication steps, unassigned tasks, total score, running time, time consumed, energy consumption, coverage, repeated rate, and communication composition. Utilizing this metric, the ILHCHU model is compared with the earlier baseline methodologies, and achieved the least number of communication steps at 70000, 20 unassigned tasks, a lower total score of 12000, a running time of 80, a time consumed of 185, a greater energy consumption figure of 750, and a coverage rate and repeated coverage rate of 97 and 33, respectively, as well as communication steps of 5000 and a communication composition of 8 all accomplished by our proposed ILHCHU model. The hybrid UAV-assisted communication system displays the potential to improve connectivity, optimize resource usage, and monitor the system environment, positioning it as a key solution for 6G edge networks in wireless powered IoT applications.
Keywords:
Unmanned Aerial Vehicle (UAV), Intelligent Graph Learning Model (IGLM), 6G Edge Network, K means Clustering Algorithm, Wireless Powered IoT.
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