Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limita...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10045685/ |
| _version_ | 1797894968873844736 |
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| author | Soohyun Park Chanyoung Park Soyi Jung Jae-Hyun Kim Joongheon Kim |
| author_facet | Soohyun Park Chanyoung Park Soyi Jung Jae-Hyun Kim Joongheon Kim |
| author_sort | Soohyun Park |
| collection | DOAJ |
| description | In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limitation in UAVs and autonomous object recognition (for abnormal behavior detection) deep learning performance in infrastructure/towers. To overcome the power limitation of UAVs, this paper proposes a novel aerial scheduling algorithm between multi-UAVs and multi-towers where the towers conduct wireless power transfer toward UAVs. In addition, to take care of the high-performance learning model training in towers, we also propose a data delivery scheme which makes UAVs deliver the training data to the towers fairly to prevent problems due to data imbalance (e.g., huge computation overhead caused by larger data delivery or overfitting from less data delivery). Therefore, this paper proposes a novel workload-aware scheduling algorithm between multi-towers and multi-UAVs for joint power-charging from towers to their associated UAVs and training data delivery from UAVs to their associated towers. To compute the workload-aware optimal scheduling decisions in each unit time, our solution approach for the given scheduling problem is designed based on Markov decision process (MDP) to deal with (i) time-varying low-complexity computation and (ii) pseudo-polynomial optimality. As shown in performance evaluation results, our proposed algorithm ensures (i) sufficient times for resource exchanges between towers and UAVs, (ii) the most even and uniform data collection during the processes compared to the other algorithms, and (iii) the performance of all towers convergence to optimal levels. |
| first_indexed | 2024-04-10T07:19:00Z |
| format | Article |
| id | doaj.art-dee46ebb112f46028d54e616dd356697 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T07:19:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dee46ebb112f46028d54e616dd3566972023-02-25T00:00:33ZengIEEEIEEE Access2169-35362023-01-0111165331654810.1109/ACCESS.2023.324582910045685Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance NetworksSoohyun Park0Chanyoung Park1Soyi Jung2Jae-Hyun Kim3https://orcid.org/0000-0003-4716-6916Joongheon Kim4https://orcid.org/0000-0003-2126-768XDepartment of Electrical and Computer Engineering, Korea University, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Korea University, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Ajou University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Ajou University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Korea University, Seoul, South KoreaIn modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limitation in UAVs and autonomous object recognition (for abnormal behavior detection) deep learning performance in infrastructure/towers. To overcome the power limitation of UAVs, this paper proposes a novel aerial scheduling algorithm between multi-UAVs and multi-towers where the towers conduct wireless power transfer toward UAVs. In addition, to take care of the high-performance learning model training in towers, we also propose a data delivery scheme which makes UAVs deliver the training data to the towers fairly to prevent problems due to data imbalance (e.g., huge computation overhead caused by larger data delivery or overfitting from less data delivery). Therefore, this paper proposes a novel workload-aware scheduling algorithm between multi-towers and multi-UAVs for joint power-charging from towers to their associated UAVs and training data delivery from UAVs to their associated towers. To compute the workload-aware optimal scheduling decisions in each unit time, our solution approach for the given scheduling problem is designed based on Markov decision process (MDP) to deal with (i) time-varying low-complexity computation and (ii) pseudo-polynomial optimality. As shown in performance evaluation results, our proposed algorithm ensures (i) sufficient times for resource exchanges between towers and UAVs, (ii) the most even and uniform data collection during the processes compared to the other algorithms, and (iii) the performance of all towers convergence to optimal levels.https://ieeexplore.ieee.org/document/10045685/Unmanned aerial networksschedulinglearning systemssurveillanceMarkov decision process (MDP) |
| spellingShingle | Soohyun Park Chanyoung Park Soyi Jung Jae-Hyun Kim Joongheon Kim Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks IEEE Access Unmanned aerial networks scheduling learning systems surveillance Markov decision process (MDP) |
| title | Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks |
| title_full | Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks |
| title_fullStr | Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks |
| title_full_unstemmed | Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks |
| title_short | Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks |
| title_sort | workload aware scheduling using markov decision process for infrastructure assisted learning based multi uav surveillance networks |
| topic | Unmanned aerial networks scheduling learning systems surveillance Markov decision process (MDP) |
| url | https://ieeexplore.ieee.org/document/10045685/ |
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