Jiaxi Liu

Vehicular ad hoc networks (VANETs) are integral to Intelligent Transportation Systems (ITS), serving as a crucial platform for deploying applications. The overall performance of VANETs is highly dependent on the vehicles, emphasizing the importance of effective fault (vehicle crash or exit) detection methods. Traditional fault detection faces challenges due to the high mobility of vehicles and prevalent software, hardware, and communication link faults, necessitating a robust solution for timely and accurate fault detection. This paper introduces ET-FD, a novel fault detection method based on ensemble learning designed to address the unique challenges in VANETs. ET-FD satisfies stringent accuracy requirements and aims to reduce system overhead significantly. We present experimental results demonstrating the superior performance of ET-FD in terms of fault detection accuracy and false positive rates, alongside comparisons with other classification methods that highlight its reduced training time. This confirms the potential of ET-FD as an efficient and reliable solution for fault detection in VANET environments.

Deep learning has made remarkable strides in hyperspectral image (HSI) classification, significantly improving classification performance. However, the challenge of obtaining accurately labeled training samples persists, primarily due to the subjectivity of human annotators and their limited domain knowledge. This often results in erroneous labels, commonly referred to as label noise. Such noisy labels can critically impair the performance of deep learning models, making it essential to address this issue. While previous studies focused on label noise filtering and label correction, these approaches often require estimating noise rates and may inadvertently propagate noisy labels to clean labels, especially in scenarios with high noise levels. In this study, we introduce an ensemble network-based distillation (END) method specifically designed to address the challenges posed by label noise in HSI classification. The core idea is to leverage multiple base neural networks to generate an estimated label distribution from the training data. This estimated distribution is then used alongside the ground-truth labels to train the target network effectively. Moreover, we propose a parameter-adaptive loss function that balances the impact of both the estimated and ground-truth label distributions during the training process. Our approach not only simplifies architectural requirements but also integrates seamlessly into existing deep learning frameworks. Comparative experiments on four hyperspectral datasets demonstrate the effectiveness of our method, highlighting its competitive performance in the presence of label noise.

In recent years, the Internet of Things (IoT) has experienced fast-paced technological advancements, connecting numerous smart devices and sensors and enabling seamless data exchange between them. However, due to software and hardware failures, it's inevitable that many sensors will fail, which can seriously affect the availability of IoT. To address this issue, we propose a fault detection method based on the clustering algorithm in this paper. This method divides sensors into different clusters, and the cluster head can determine the status of sensors based on data from neighboring sensors within the same group. Our simulation experiments demonstrate that this fault detection method has better performance in terms of detection accuracy and false positive rate.

Vehicular ad hoc network (VANETs) improves road safety and efficiency by organizing vehicles and infrastructure to provide a platform for application deployment. The availability of vehicles and infrastructure is critical to the operation of applications. Accurate failure detector (FD) has been one of the fundamental components for maintaining high availability in VANETs. However, it is hard to find the vehicle failure accurately and timely due to the dynamic nature of VANETs caused by the high mobility of vehicles and communications link failures. Therefore, it is important to achieve an accurate FD which can cope with the high mobility of VANETs. In this paper, we propose a dead reckoning based FD, called DR-FD. It can predict the mobility of vehicle accurately and avoid the impact of link failures on the detection results by the cooperation between vehicles. Experimental results are provided to confirm that the proposed DR-FD method can achieve at most 20% reduction in detection time, 30% improvement in mistake rate and 20% improvement in overhead.

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.

The failure detector is one of the fundamental components for maintaining high availability of Vehicular Ad-hoc Networks (VANETs). However, the dynamic nature of VANETs caused by the high mobility of vehicles and communication link failures has a serious impact on the performance of failure detection. Therefore, it is very meaningful to design a suitable failure detector that can deal with the dynamic nature of VANETs well. In this paper, we propose a hierarchical failure detector based on the architecture of VANETs. This failure detector can adapt to the dynamic network conditions and meet the different Quality of Service (QoS) requirements of multiple applications in VANETs. Different from existing failure detectors, we propose a failure detector that employs a detection-result sharing mechanism and groups the nodes according to the architecture of VANETs. We evaluate our proposed failure detector by using NS2 and GT-ITM to simulate the work environment of VANETs. The experimental result shows that our proposed failure detector can improve the detection time by at most 45% and the detection accuracy by at most 25% under similar detection overhead.

Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing.