Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications
The rapid development of the Internet of Things and wireless communication has resulted in the emergence of many latency-constrained and computation-intensive applications such as surveillance, virtual reality, and disaster monitoring. To satisfy the computational demand and reduce the prolonged tra...
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10174639/ |
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| author | S. M. Asiful Huda Sangman Moh |
| author_facet | S. M. Asiful Huda Sangman Moh |
| author_sort | S. M. Asiful Huda |
| collection | DOAJ |
| description | The rapid development of the Internet of Things and wireless communication has resulted in the emergence of many latency-constrained and computation-intensive applications such as surveillance, virtual reality, and disaster monitoring. To satisfy the computational demand and reduce the prolonged transmission delay to the cloud, mobile edge computing (MEC) has evolved as a potential candidate that can improve task completion efficiency in a reliable fashion. Owing to its high mobile nature and ease of use, as promising candidates, unmanned aerial vehicles (UAVs) can be incorporated with MEC to support such computation-intensive and latency-critical applications. However, determining the ideal offloading decision for the UAV on basis of the task characteristics still remains a crucial challenge. In this paper, we investigate a surveillance application scenario of a hierarchical UAV swarm that includes an UAV-enabled MEC with a team of UAVs surveilling the area to be monitored. To determine the optimal offloading policy, we propose a deep reinforcement learning based computation offloading (DRLCO) scheme using double deep Q-learning, which minimizes the weighted sum cost by jointly considering task execution delay and energy consumption. A performance study shows that the proposed DRLCO technique significantly outperforms conventional schemes in terms of offloading cost, energy consumption, and task execution delay. The better convergence and effectiveness of the proposed method over conventional schemes are also demonstrated. |
| format | Article |
| id | doaj-art-efd9007618b341dcb2e7a0fccd2f59e9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-efd9007618b341dcb2e7a0fccd2f59e92025-08-20T03:43:55ZengIEEEIEEE Access2169-35362023-01-0111682696828510.1109/ACCESS.2023.329293810174639Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance ApplicationsS. M. Asiful Huda0https://orcid.org/0000-0002-7192-1654Sangman Moh1https://orcid.org/0000-0001-9175-3400Department of Computer Engineering, Chosun University, Gwangju, South KoreaDepartment of Computer Engineering, Chosun University, Gwangju, South KoreaThe rapid development of the Internet of Things and wireless communication has resulted in the emergence of many latency-constrained and computation-intensive applications such as surveillance, virtual reality, and disaster monitoring. To satisfy the computational demand and reduce the prolonged transmission delay to the cloud, mobile edge computing (MEC) has evolved as a potential candidate that can improve task completion efficiency in a reliable fashion. Owing to its high mobile nature and ease of use, as promising candidates, unmanned aerial vehicles (UAVs) can be incorporated with MEC to support such computation-intensive and latency-critical applications. However, determining the ideal offloading decision for the UAV on basis of the task characteristics still remains a crucial challenge. In this paper, we investigate a surveillance application scenario of a hierarchical UAV swarm that includes an UAV-enabled MEC with a team of UAVs surveilling the area to be monitored. To determine the optimal offloading policy, we propose a deep reinforcement learning based computation offloading (DRLCO) scheme using double deep Q-learning, which minimizes the weighted sum cost by jointly considering task execution delay and energy consumption. A performance study shows that the proposed DRLCO technique significantly outperforms conventional schemes in terms of offloading cost, energy consumption, and task execution delay. The better convergence and effectiveness of the proposed method over conventional schemes are also demonstrated.https://ieeexplore.ieee.org/document/10174639/Aerial computingcomputation offloadingdeep reinforcement learningdouble deep Q-learningmobile edge computingmulti-agent reinforcement learning |
| spellingShingle | S. M. Asiful Huda Sangman Moh Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications IEEE Access Aerial computing computation offloading deep reinforcement learning double deep Q-learning mobile edge computing multi-agent reinforcement learning |
| title | Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications |
| title_full | Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications |
| title_fullStr | Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications |
| title_full_unstemmed | Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications |
| title_short | Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications |
| title_sort | deep reinforcement learning based computation offloading in uav swarm enabled edge computing for surveillance applications |
| topic | Aerial computing computation offloading deep reinforcement learning double deep Q-learning mobile edge computing multi-agent reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10174639/ |
| work_keys_str_mv | AT smasifulhuda deepreinforcementlearningbasedcomputationoffloadinginuavswarmenablededgecomputingforsurveillanceapplications AT sangmanmoh deepreinforcementlearningbasedcomputationoffloadinginuavswarmenablededgecomputingforsurveillanceapplications |