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Edge Machine Learning for AI-Enabled IoT Devices: A Review
Sensors, 2020 In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users.
Massimo Merenda +2 more
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An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices
IEEE Access, 2021 Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes
Rei Ito +2 more
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Enhancing YOLOv5 for Autonomous Driving: Efficient Attention-Based Object Detection on Edge Devices [PDF]
Journal of ImagingOn-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their ...
Mortda A. A. Adam, Jules R. Tapamo
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Dual-Stage Super-Resolution for Edge Devices
IEEE Access, 2023 To reduce memory usage, edge devices such as TVs use Super-resolution(SR) with dedicated hardware networks. Dedicated hardware has the disadvantage of being difficult to change and difficult to improve performance.
Saem Park +3 more
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Blockchains for constrained edge devices
Blockchain: Research and Applications, 2020 Today’s networks are seeing a large influx of Internet connected devices that reside primarily on the edge of the network. Many of these devices, such as Internet of Things (IoT) devices, are resource constrained both by storage capacity and power ...
Antonyo Douglas +4 more
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IEEE Access, 2022 The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and
Lorenzo Papa +3 more
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Collaborative Task Scheduling for IoT-Assisted Edge Computing
IEEE Access, 2020 The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains.
Youngjin Kim +4 more
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Sensors, 2021 As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years.
Yu-Bang Chang +3 more
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Sensors, 2023 Tiny machine learning (TinyML) has become an emerging field according to the rapid growth in the area of the internet of things (IoT). However, most deep learning algorithms are too complex, require a lot of memory to store data, and consume an enormous ...
Kyungho Kim +4 more
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Edge Computing in IoT–Enabled Honeybee Monitoring for the Detection of Varroa Destructor
International Journal of Applied Mathematics and Computer Science, 2022 Among many important functions, bees play a key role in food production. Unfortunately, worldwide bee populations have been decreasing since 2007. One reason for the decrease of adult worker bees is varroosis, a parasitic disease caused by the Varroa ...
Wachowicz Anna +4 more
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