Yueling Che, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan, Victor C. M. Leung
Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.
{"title":"UAV-Aided Wireless Energy Transfer for Sustaining Internet of Everything in 6G","authors":"Yueling Che, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan, Victor C. M. Leung","doi":"10.3390/drones7100628","DOIUrl":"https://doi.org/10.3390/drones7100628","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of unmanned aerial vehicles (UAVs) in many fields of expertise has increased over recent years. As such, UAVs used for monitoring coastline changes are also becoming more frequent, more practical, and more effective, whether for conducting academic work or for business and administrative activities. This study thus addresses the use of unmanned aerial vehicles (UAVs) for monitoring changing coastlines, in particular morphological coastal changes caused by rising sea levels, reductions in sediment load, or changes produced by engineering infrastructure. For this objective, a bibliometric analysis was conducted on the basis of 160 research articles published in the last 20 years, using the Web of Science database. The analysis shows that the countries leading the way in researching coastline changes with UAVs are the United States, France, South Korea, and Spain. In addition, this study provides data on the most influential publications and authors on this topic and on research trends. It further highlights the value addition made by UAVs to monitoring coastline changes.
近年来，无人驾驶飞行器(uav)在许多专业领域的使用有所增加。因此，无论是用于学术工作还是用于商业和行政活动，用于监测海岸线变化的无人机也变得越来越频繁，越来越实用，越来越有效。因此，本研究涉及使用无人驾驶飞行器(uav)监测不断变化的海岸线，特别是由海平面上升、沉积物负荷减少或工程基础设施产生的变化引起的海岸形态变化。为此，使用Web of Science数据库，对过去20年发表的160篇研究论文进行了文献计量学分析。分析表明，在利用无人机研究海岸线变化方面处于领先地位的国家是美国、法国、韩国和西班牙。此外，本研究还提供了有关该主题最具影响力的出版物和作者以及研究趋势的数据。它进一步强调了无人机在监测海岸线变化方面的附加价值。
{"title":"The Use of UAVs for Morphological Coastal Change Monitoring—A Bibliometric Analysis","authors":"Jorge Novais, António Vieira, António Bento-Gonçalves, Sara Silva, Saulo Folharini, Tiago Marques","doi":"10.3390/drones7100629","DOIUrl":"https://doi.org/10.3390/drones7100629","url":null,"abstract":"The use of unmanned aerial vehicles (UAVs) in many fields of expertise has increased over recent years. As such, UAVs used for monitoring coastline changes are also becoming more frequent, more practical, and more effective, whether for conducting academic work or for business and administrative activities. This study thus addresses the use of unmanned aerial vehicles (UAVs) for monitoring changing coastlines, in particular morphological coastal changes caused by rising sea levels, reductions in sediment load, or changes produced by engineering infrastructure. For this objective, a bibliometric analysis was conducted on the basis of 160 research articles published in the last 20 years, using the Web of Science database. The analysis shows that the countries leading the way in researching coastline changes with UAVs are the United States, France, South Korea, and Spain. In addition, this study provides data on the most influential publications and authors on this topic and on research trends. It further highlights the value addition made by UAVs to monitoring coastline changes.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinxin Zuo, Ruohan Cao, Jiahao Qi, Peng Gao, Ziping Wang, Jin Li, Long Zhang, Yueming Lu
In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.
{"title":"A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes","authors":"Jinxin Zuo, Ruohan Cao, Jiahao Qi, Peng Gao, Ziping Wang, Jin Li, Long Zhang, Yueming Lu","doi":"10.3390/drones7100627","DOIUrl":"https://doi.org/10.3390/drones7100627","url":null,"abstract":"In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sibo Zhao, Jianwen Zhu, Weimin Bao, Xiaoping Li, Haifeng Sun
In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.
{"title":"A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning","authors":"Sibo Zhao, Jianwen Zhu, Weimin Bao, Xiaoping Li, Haifeng Sun","doi":"10.3390/drones7100626","DOIUrl":"https://doi.org/10.3390/drones7100626","url":null,"abstract":"In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article describes the process of integrating one of the most commonly used laser methane detectors, the Laser Methane mini (LMm), and a multi-rotor unmanned aerial vehicle (UAV) based on the Pixhawk flight controller to create an unmanned aerial system designed to detect methane leakages from the air. The integration is performed via the LaserHub+, a newly developed device which receives data from the laser methane detector, decodes it and transmits it to the flight controller with the protocol used by the ArduPilot platform for laser rangefinders. The user receives a single data array from the UAV flight controller that contains both the values of the methane concentrations measured by the detector, and the co-ordinates of the corresponding measurement points in three-dimensional space. The transmission of data from the UAV is carried out in real time. It is shown in this project that the proposed technical solution (the LaserHub+) has clear advantages over not only similar serial commercial solutions (e.g., the SkyHub complex by SPH Engineering) but also experimental developments described in the scientific literature. The main reason is that LaserHub+ does not require a deep customization of the methane detector or the placement of additional complex devices on board the UAV. Tests using it were carried out in aerial gas surveys of a number of municipal solid waste disposal sites in Russia. The device has a low cost and is easy for the end user to assemble, connect to the UAV and set up. The authors believe that LaserHub+ can be recommended as a mass solution for aerial surveys of methane sources. Information is provided on the approval of LaserHub+ for aerial gas surveys of a number of Russian municipal waste disposal facilities.
{"title":"Integrating a UAV System Based on Pixhawk with a Laser Methane Mini Detector to Study Methane Emissions","authors":"Timofey Filkin, Iliya Lipin, Natalia Sliusar","doi":"10.3390/drones7100625","DOIUrl":"https://doi.org/10.3390/drones7100625","url":null,"abstract":"This article describes the process of integrating one of the most commonly used laser methane detectors, the Laser Methane mini (LMm), and a multi-rotor unmanned aerial vehicle (UAV) based on the Pixhawk flight controller to create an unmanned aerial system designed to detect methane leakages from the air. The integration is performed via the LaserHub+, a newly developed device which receives data from the laser methane detector, decodes it and transmits it to the flight controller with the protocol used by the ArduPilot platform for laser rangefinders. The user receives a single data array from the UAV flight controller that contains both the values of the methane concentrations measured by the detector, and the co-ordinates of the corresponding measurement points in three-dimensional space. The transmission of data from the UAV is carried out in real time. It is shown in this project that the proposed technical solution (the LaserHub+) has clear advantages over not only similar serial commercial solutions (e.g., the SkyHub complex by SPH Engineering) but also experimental developments described in the scientific literature. The main reason is that LaserHub+ does not require a deep customization of the methane detector or the placement of additional complex devices on board the UAV. Tests using it were carried out in aerial gas surveys of a number of municipal solid waste disposal sites in Russia. The device has a low cost and is easy for the end user to assemble, connect to the UAV and set up. The authors believe that LaserHub+ can be recommended as a mass solution for aerial surveys of methane sources. Information is provided on the approval of LaserHub+ for aerial gas surveys of a number of Russian municipal waste disposal facilities.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135301588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane, William Guo
Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.
{"title":"Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study","authors":"Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane, William Guo","doi":"10.3390/drones7100624","DOIUrl":"https://doi.org/10.3390/drones7100624","url":null,"abstract":"Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135300610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the past several years, significant progress has been made in object tracking, but challenges persist in tracking objects in high-resolution images captured from drones. Such images usually contain very tiny objects, and the movement of the drone causes rapid changes in the scene. In addition, the computing power of mission computers on drones is often insufficient to achieve real-time processing of deep learning-based object tracking. This paper presents a real-time on-drone pedestrian tracker that takes as the input 4K aerial images. The proposed tracker effectively hides the long latency required for deep learning-based detection (e.g., YOLO) by exploiting both the CPU and GPU equipped in the mission computer. We also propose techniques to minimize detection loss in drone-captured images, including a tracker-assisted confidence boosting and an ensemble for identity association. In our experiments, using real-world inputs captured by drones at a height of 50 m, the proposed method with an NVIDIA Jetson TX2 proves its efficacy by achieving real-time detection and tracking in 4K video streams.
{"title":"Towards Real-Time On-Drone Pedestrian Tracking in 4K Inputs","authors":"Chanyoung Oh, Moonsoo Lee, Chaedeok Lim","doi":"10.3390/drones7100623","DOIUrl":"https://doi.org/10.3390/drones7100623","url":null,"abstract":"Over the past several years, significant progress has been made in object tracking, but challenges persist in tracking objects in high-resolution images captured from drones. Such images usually contain very tiny objects, and the movement of the drone causes rapid changes in the scene. In addition, the computing power of mission computers on drones is often insufficient to achieve real-time processing of deep learning-based object tracking. This paper presents a real-time on-drone pedestrian tracker that takes as the input 4K aerial images. The proposed tracker effectively hides the long latency required for deep learning-based detection (e.g., YOLO) by exploiting both the CPU and GPU equipped in the mission computer. We also propose techniques to minimize detection loss in drone-captured images, including a tracker-assisted confidence boosting and an ensemble for identity association. In our experiments, using real-world inputs captured by drones at a height of 50 m, the proposed method with an NVIDIA Jetson TX2 proves its efficacy by achieving real-time detection and tracking in 4K video streams.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Min, Abdukodir Khakimov, Abdelhamied A. Ateya, Mohammed ElAffendi, Ammar Muthanna, Ahmed A. Abd El-Latif, Mohammed Saleh Ali Muthanna
The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.
{"title":"Dynamic Offloading in Flying Fog Computing: Optimizing IoT Network Performance with Mobile Drones","authors":"Wei Min, Abdukodir Khakimov, Abdelhamied A. Ateya, Mohammed ElAffendi, Ammar Muthanna, Ahmed A. Abd El-Latif, Mohammed Saleh Ali Muthanna","doi":"10.3390/drones7100622","DOIUrl":"https://doi.org/10.3390/drones7100622","url":null,"abstract":"The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahsan Rafiq, Reem Alkanhel, Mohammed Saleh Ali Muthanna, Evgeny Mokrov, Ahmed Aziz, Ammar Muthanna
An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.
{"title":"Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks","authors":"Ahsan Rafiq, Reem Alkanhel, Mohammed Saleh Ali Muthanna, Evgeny Mokrov, Ahmed Aziz, Ammar Muthanna","doi":"10.3390/drones7100619","DOIUrl":"https://doi.org/10.3390/drones7100619","url":null,"abstract":"An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aleksander Suti, Gianpietro Di Rito, Giuseppe Mattei
This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from the design, causing internal force-fighting and reduced efficiency. This study demonstrates that, by only elaborating the measurements of speed and direct/quadrature currents of the stators during motor acceleration/deceleration, online estimations of demagnetization and electrical angle misalignment can be obtained, thus permitting the evaluation of the imbalance and total torque of the system. A relevant outcome is that the technique can be used for developing both signal-based and model-based monitoring schemes. Starting from physical first-principles, a nonlinear model of the propulsion system, including demagnetization and electrical angle misalignment, is developed in order to analytically derive the relationships between monitoring inputs (currents and speed) and outputs (degradations). The model is experimentally validated using a system prototype characterized by asymmetrical demagnetization and electrical angle misalignment. Finally, the monitoring effectiveness is assessed by simulating UAV flight manoeuvres with the experimentally validated model: injecting different levels of degradations and evaluating the torque imbalance.
{"title":"Condition Monitoring of the Torque Imbalance in a Dual-Stator Permanent Magnet Synchronous Motor for the Propulsion of a Lightweight Fixed-Wing UAV","authors":"Aleksander Suti, Gianpietro Di Rito, Giuseppe Mattei","doi":"10.3390/drones7100618","DOIUrl":"https://doi.org/10.3390/drones7100618","url":null,"abstract":"This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from the design, causing internal force-fighting and reduced efficiency. This study demonstrates that, by only elaborating the measurements of speed and direct/quadrature currents of the stators during motor acceleration/deceleration, online estimations of demagnetization and electrical angle misalignment can be obtained, thus permitting the evaluation of the imbalance and total torque of the system. A relevant outcome is that the technique can be used for developing both signal-based and model-based monitoring schemes. Starting from physical first-principles, a nonlinear model of the propulsion system, including demagnetization and electrical angle misalignment, is developed in order to analytically derive the relationships between monitoring inputs (currents and speed) and outputs (degradations). The model is experimentally validated using a system prototype characterized by asymmetrical demagnetization and electrical angle misalignment. Finally, the monitoring effectiveness is assessed by simulating UAV flight manoeuvres with the experimentally validated model: injecting different levels of degradations and evaluating the torque imbalance.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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