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As one of the most complex parts in manufacturing systems, scheduling plays an important role in the efficient allocation of resources to meet individual customization requirements. However, due to the uncertain disruptions (e.g., task arrival time, service breakdown duration) of manufacturing processes, how to respond to various dynamics in manufacturing to keep the scheduling process moving forward smoothly and efficiently is becoming a major challenge in dynamic manufacturing scheduling. To solve such a problem, a wide spectrum of artificial intelligence techniques have been developed to (1) accurately construct dynamic scheduling models that can represent both personalized customer needs and uncertain provider capabilities and (2) efficiently obtain a qualified schedule within a limited time. From these two perspectives, this article systemically makes a state-of-the-art literature survey on the application of these artificial intelligence techniques in dynamic manufacturing modeling and scheduling. It first introduces two types of dynamic scheduling problems that consider service-and task-related disruptions in the manufacturing process, respectively, followed by a bibliometric analysis of artificial intelligence techniques for dynamic manufacturing scheduling. Next, various kinds of artificial-intelligence-enabled schedulers for solving dynamic scheduling problems including both directed heuristics and autonomous learning methods are reviewed, which strive not only to quickly obtain optimized solutions but also to effectively achieve the adaption to dynamics. Finally, this article further elaborates on the future opportunities and challenges of using artificialintelligence- enabled schedulers to solve complex dynamic scheduling problems. In summary, this survey aims to present a thorough and organized overview of artificial-intelligence-enabled dynamic manufacturing scheduling and shed light on some related research directions that are worth studying in the future. [ABSTRACT FROM AUTHOR]

Autonomous robots are increasingly served in real-world unstructured human environments with complex long-horizon tasks, such as restaurant serving and office delivery. Task and motion planning (TAMP) is a recent research method in Artificial Intelligence Planning for these applications. TAMP integrates high-level abstract reasoning with the low-level geometric feasibility check and thus is more comprehensive than traditional task planning methods. While regular TAMP approaches are challenged by different types of uncertainties and the generalization of various applications when implemented in real-world scenarios. This article systematically reviews the most relevant approaches to TAMP and classifies them according to their features and emphasis; it categorizes the challenges and presents online TAMP and machine learning-based TAMP approaches for addressing them. [ABSTRACT FROM AUTHOR]

With the development of real-time critical systems, the ever-increasing communication data traffic puts forward high-bandwidth and low-delay requirements for communication networks. Therefore, various real-time Ethernet protocols have been proposed, but these protocols are not compatible with each other. The IEEE 802.1 Working Group developed standardized protocols named Audio Video Bridging (AVB) in 2005, and renamed it Time-Sensitive Networking (TSN) later. TSN not only adds new features but also retains the original functions of AVB. Proposing real-time Ethernet modeling and design methodologies is the key to meeting high-bandwidth and low-delay communication requirements. This article surveys the modeling from AVB to TSN, mainly including: (1) AVB and TSN modeling; (2) end-to-end delay modeling; (3) real-time scheduling modeling; (4) reliability modeling; and (5) security modeling. Based on these models, this article surveys the recent advances in real-time Ethernet design methodologies from AVB to TSN: (1) end-to-end delay analysis from AVB to TSN; (2) real-time scheduling from AVB to TSN; (3) reliability-aware design for TSN; and (4) security-aware design for TSN. Among the above four points, the last two points are only for TSN, because AVB lacks reliability and security mechanisms. This article further takes the automotive use case as an example to discuss the application of TSN in automobiles. Finally, this article discusses the future trends of TSN. By surveying the recent advances and future trends, we hope to provide references for researchers interested in real-time Ethernet modeling and design methodologies for AVB and TSN. [ABSTRACT FROM AUTHOR]

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19. [ABSTRACT FROM AUTHOR]

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed. [ABSTRACT FROM AUTHOR]

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers' power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers. [ABSTRACT FROM AUTHOR]

With the development of wireless technology, various emerging mobile applications are attracting significant attention and drastically changing our daily lives. Applications such as augmented reality and object recognition demand stringent delay and powerful processing capability, which exerts enormous pressure on mobile devices with limited resources and energy. In this article, a survey of techniques for mobile device energy optimization is presented in a hierarchy of device design and operation, computation offloading, wireless data transmission, and cloud execution of offloaded computation. Energy management strategies for mobile devices from hardware and software aspects are first discussed, followed by energy-efficient computation offloading frameworks for mobile applications that trade application response time for device energy consumption. Then, techniques for efficient wireless data communication to reduce transmission energy are summarized. Finally, the execution mechanisms of application components or tasks in various clouds are further described to provide energy-saving opportunities for mobile devices. We classify the research works based on key characteristics of devices and applications to emphasize their similarities and differences. We hope that this survey will give insights to researchers into energy management mechanisms on mobile devices, and emphasize the crucial importance of optimizing device energy consumption for more research efforts in this area. [ABSTRACT FROM AUTHOR]

As the demand for computing resources grows, cloud computing becomes more and more popular as a pay-as-you-go model, in which the computing resources and services are provided to cloud users efficiently. For cloud providers, the typical goal is to maximize their profits. However, maximizing profits in a highly competitive cloud market is a huge challenge for cloud providers. In this article, a survey of profit optimization techniques is proposed to increase cloud provider profitability through service quality improvement, service pricing, energy consumption reduction, and virtual network function (VNF) deployment. The strategy of improving user service quality is discussed first, followed by the pricing strategy for cloud resources to maximize revenue. Then, this article summarizes the techniques for cloud data centers to reduce server power consumption. Finally, various heuristic algorithms for VNF deployment in the cloud are further described to reduce the cost of cloud providers while maintaining performance. We classify research works based on components of profit and methods used to demonstrate similarities and differences in these studies. We hope this survey will provide researchers with insights into cloud profit optimization techniques. [ABSTRACT FROM AUTHOR]