Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid development of Internet of Things (IoT) and cloud technologies together with recent advances in machine learning offered various cloud-based data-driven approaches to automatic anomaly detection. However, the challenges introduced by the different types of equipment in rail transit systems with highly diverse data distributions and the lack of labeled anomaly data have not been sufficiently addressed. In this article, we attempt to cope with such challenges by proposing an improved long short term memory (LSTM)-based time-series anomaly detection scheme. The key elements of the proposed scheme include an improved LSTM model that may achieve more accurate time-series prediction for various rail transit devices and a method for determining an appropriate error threshold for detecting anomalies based on the prediction errors. In order to further enhance anomaly detection performance, we also propose a pruning algorithm for reducing the number of false anomalies. Our method does not rely on scarce anomaly labels but dynamically determines a threshold of prediction errors to identify anomalies; therefore, it overcomes the challenge of the extremely uneven distribution of rail transit data. We conducted extensive experiments in a real metro operation environment for performance evaluation. The experiment results prove the effectiveness of the proposed scheme and show a superior performance of the scheme compared to existing anomaly detection methods.

Edge computing can provide high bandwidth and low-latency service for big data tasks by leveraging the edge side’s computing, storage, and network resources. With the development of microservice and docker technology, service providers can flexibly and dynamically cache microservice at the edge side to respond efficiently with limited resources. Automatically caching needed services on the nearest edge nodes and dynamically scheduling users’ requests can realize that computing power and software services flow with the users to provide continuous services. However, achieving the goal needs to overcome many challenges, such as the significant fluctuation of user devices’ requests at the edge side and the lack of collaboration among edge nodes. In this article, dynamic computing power scheduling and collaborative task scheduling among edge nodes are comprehensively developed. The problem is considered a multiobjective optimization problem, including sequentially minimizing the deadline missing rate of requests and the average task completion time. We propose an adaptive mechanism for dynamically collaborative computing power and task scheduling (ADCS) in the edge environment to solve this problem. It adopts the greedy decision method to schedule computing tasks to meet their deadline requirements. At the same time, it uses the best-fit method to adjust the computing resources according to the changes of users’ requests. The simulation results show that ADCS can decrease the deadline missing rate and reduce the average completion time. Compared with DSR and CoDSR, the deadline missing rate is reduced by 59.91% and 19.95%, respectively. The average completion time is decreased by 37.87% and 6.71%.

In Industry 4.0 and Internet of Things (IoT) environments, the heterogeneous edge-cloud computing paradigm can provide a more proper solution to deploy scientific workflows compared to cloud computing or other traditional distributed computing. Owing to the different sizes of scientific datasets and the privacy issue concerning some of these datasets, it is essential to find a data placement strategy that can minimize data transmission time. Some state-of-the-art data placement strategies combine edge computing and cloud computing to distribute scientific datasets. However, the dynamic distribution of newly generated datasets to appropriate datacenters and exiting the spent datasets are still a challenge during workflows execution. To address this challenge, this study not only constructs a data placement model that includes shared datasets within the individual and among multiple workflows across various geographical regions, but also proposes a data placement strategy (DYM-RL-DPS) based on algorithms of two stages. First, during the build-time stage of workflows, we use the discrete particle swarm optimization algorithm with differential evolution to pre-allocate initial datasets to proper datacenters. Then, we reformulate the dynamic datasets distribution problem as a Markov decision process and provide a reinforcement learning–based approach to learn the data placement strategy in the runtime stage of scientific workflows. Through using the heterogeneous edge-cloud computing architecture to simulate IoT environments, we designed comprehensive experiments to demonstrate the superiority of DYM-RL-DPS. The results of our strategy can effectively reduce the data transmission time as compared to other strategies.

Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users’ perspectives, users have to personally select the optimal Web services according to quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, QoS data vary over time due to changes in user reputation; therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called OPRC, to efficiently provide a personalized reputation for each user. Based on the users’ observed QoS data, OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.