You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (i.e., COCO and PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple Intersection over Union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. It was found that YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.

This paper presents a scalable page cache called ScaleCache for improving SSD scalability. Specifically, we first propose a concurrent data structure of page cache based on XArray (ccXArray) to enable access and update the page cache concurrently. Second, we introduce a direct page flush (dflush) which directly flushes pages to storage devices in a parallel and opportunistic manner. We implement ScaleCache with two techniques in the Linux kernel and evaluate it on a 64-core machine with eight NVMe SSDs. Our evaluations show that ScaleCache improves the performance of Linux file systems by up to 6.81× and 4.50× compared with the existing scheme and scalable scheme for multiple SSDs, respectively.

The Internet of Things (IoT) has been an emerging critical technology platform enabling the development and deployment of smart devices to solve real-world challenges and issues. In addition, IoT applications have gathered various and numerous data from various sources, such as sensor data from IoT devices, system data, status information of IoT devices, and others. However, the quality of sensor data should be thoroughly verified to exploit the benefits of sensor data fully. Due to the importance of sensor data quality, we comprehensively provide a survey on the verification of IoT sensor data in this paper. Thus, we conduct a survey of sensor data verification in IoT regarding six aspects. We focus on 1) anomaly classification, 2) sensor data verification frameworks, 3) sensor data verification methods (including anomaly detection and anomaly correction), 4) evaluation methods to assess sensor data verification methods, 5) technology and tools to build verification tests of sensor data, and 6) challenges and future research directions. In addition, we identify the advantages and disadvantages of the methods in anomaly detection and correction. Hence, this survey provides a comprehensive and better understanding of sensor data verification in IoT, building background knowledge in related topics.

Internet of things (IoT) systems are collecting an enormous amount of sensor data which in turn play a critical role in their decision-making process. However, the more sensor data are collected, the higher probability the faulty data occurs. The existence of faulty data may drive any system to make incorrect decisions and actions. It is clear that the faulty data should be detected and corrected as earlier as possible. In other words, the sensor data should be verified in the collecting steps. To best of our knowledge, the sensor data verification in the collecting process has not been well investigated. This research proposes a framework to address this issue right before the sensor data are stored. The verification makes use of a data forecasting technique to estimate the correct range of values for each received point of sensor data. The proposed system was implemented on a real test-bed on a Raspberry Pi 3. The experimental results show the proposed system can detect and correct faulty sensor data in less than 50ms. The accuracy of the proposed method is eighty five percent on average.

This paper introduces a dynamic context-aware load balancing mechanism called EdgeUP, which leverages dynamic contexts of both edge nodes and IoT devices to balance tasks on edge nodes optimally. To perform this, we devised a lightweight load balancer to allocate tasks optimally based on CPU utilization across edge nodes while satisfying the task priority of IoT devices. We implemented EdgeUP on real edge devices (i.e., Raspberry Pi 400) and evaluated it in terms of the average CPU utilization of edge nodes, idle CPU distance, and execution time. The experimental results show that EdgeUP outperforms three conventional load balancing algorithms, including round-robin, hill climbing, and K-Means, by 5.6×, 2.1×, and 3.6×, respectively.

You Only Look One (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, choosing selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (e.g., COCO, PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple intersection-over-union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. Thus, YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.