Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration
The computing-intensive tasks generated by Internet of Things devices cannot be handled alone by themselves due to limitations in battery and processing power. An appropriate approach to this problem is the Internet of Drones (IoDs) with edge computing capabilities, which can offload the created tas...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10950141/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850148348477046784 |
|---|---|
| author | Ahmad Arsalan Tariq Umer Rana Asif Rehman Byung-Seo Kim |
| author_facet | Ahmad Arsalan Tariq Umer Rana Asif Rehman Byung-Seo Kim |
| author_sort | Ahmad Arsalan |
| collection | DOAJ |
| description | The computing-intensive tasks generated by Internet of Things devices cannot be handled alone by themselves due to limitations in battery and processing power. An appropriate approach to this problem is the Internet of Drones (IoDs) with edge computing capabilities, which can offload the created tasks from IoT devices to IoDs. To improve sustainability by maximizing energy efficiency, minimizing duplicate service migrations, and guaranteeing dynamic task offloading in UAV-supported IoD networks, this paper proposes a Federated Digital aided Internet of Drones (FD-IoD) architecture. The proposed framework guarantees the long-term viability of IoD-based edge networks by combining digital twin technology with federated deep reinforcement learning. The FD-IoD framework integrates energy harvesting algorithms and optimizes mobility-aware resource management to extend drone lifespan and reduce unnecessary computational overheads. To adjust to various IoT environments, the framework uses a dual-layer optimization approach that combines local agent learning with global decision-making via digital twin. The framework outperforms current benchmarks by up to 40% in energy efficiency, lower service migration rates, and faster task completion rates, as shown by extensive simulations. Additionally, the proposed framework guarantees decreased latency, efficient resource use, and queue stability even in heavy demand. |
| format | Article |
| id | doaj-art-b11c08c64f194414b0801e08d0ee53e5 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b11c08c64f194414b0801e08d0ee53e52025-08-20T02:27:16ZengIEEEIEEE Access2169-35362025-01-0113644596447210.1109/ACCESS.2025.355843910950141Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service MigrationAhmad Arsalan0https://orcid.org/0000-0002-9406-398XTariq Umer1https://orcid.org/0000-0002-3333-8142Rana Asif Rehman2https://orcid.org/0000-0003-3263-4211Byung-Seo Kim3https://orcid.org/0000-0001-9824-1950Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanFAST School of Computing, National University of Computer and Emerging Sciences, Lahore Campus, Lahore, PakistanDepartment of Software and Communications Engineering, Hongik University, Sejong City, South KoreaThe computing-intensive tasks generated by Internet of Things devices cannot be handled alone by themselves due to limitations in battery and processing power. An appropriate approach to this problem is the Internet of Drones (IoDs) with edge computing capabilities, which can offload the created tasks from IoT devices to IoDs. To improve sustainability by maximizing energy efficiency, minimizing duplicate service migrations, and guaranteeing dynamic task offloading in UAV-supported IoD networks, this paper proposes a Federated Digital aided Internet of Drones (FD-IoD) architecture. The proposed framework guarantees the long-term viability of IoD-based edge networks by combining digital twin technology with federated deep reinforcement learning. The FD-IoD framework integrates energy harvesting algorithms and optimizes mobility-aware resource management to extend drone lifespan and reduce unnecessary computational overheads. To adjust to various IoT environments, the framework uses a dual-layer optimization approach that combines local agent learning with global decision-making via digital twin. The framework outperforms current benchmarks by up to 40% in energy efficiency, lower service migration rates, and faster task completion rates, as shown by extensive simulations. Additionally, the proposed framework guarantees decreased latency, efficient resource use, and queue stability even in heavy demand.https://ieeexplore.ieee.org/document/10950141/Digital twinInternet of Dronesfederated reinforcement learningtask offloadingservice migrationenergy efficiency |
| spellingShingle | Ahmad Arsalan Tariq Umer Rana Asif Rehman Byung-Seo Kim Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration IEEE Access Digital twin Internet of Drones federated reinforcement learning task offloading service migration energy efficiency |
| title | Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration |
| title_full | Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration |
| title_fullStr | Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration |
| title_full_unstemmed | Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration |
| title_short | Next-Gen Internet of Drones: Federated Learning and Digital Twin Synergy for Energy-Efficient Task Allocation and Seamless Service Migration |
| title_sort | next gen internet of drones federated learning and digital twin synergy for energy efficient task allocation and seamless service migration |
| topic | Digital twin Internet of Drones federated reinforcement learning task offloading service migration energy efficiency |
| url | https://ieeexplore.ieee.org/document/10950141/ |
| work_keys_str_mv | AT ahmadarsalan nextgeninternetofdronesfederatedlearninganddigitaltwinsynergyforenergyefficienttaskallocationandseamlessservicemigration AT tariqumer nextgeninternetofdronesfederatedlearninganddigitaltwinsynergyforenergyefficienttaskallocationandseamlessservicemigration AT ranaasifrehman nextgeninternetofdronesfederatedlearninganddigitaltwinsynergyforenergyefficienttaskallocationandseamlessservicemigration AT byungseokim nextgeninternetofdronesfederatedlearninganddigitaltwinsynergyforenergyefficienttaskallocationandseamlessservicemigration |