Software-defined networking (SDN) has emerged as a new network paradigm that promises control/data plane separation and centralized network control. While these features simplify network management and enable innovative networking, they give rise to persistent concerns about reliability. The new paradigm suffers from the disadvantage that various network faults may consistently undermine the reliability of such a network, and such faults are often new and difficult to resolve with existing solutions. To ensure SDN reliability, fault management, which is concerned with detecting, localizing, correcting and preventing faults, has become a key component in SDN networks. Although many SDN fault management solutions have been proposed, we find that they often resolve SDN faults from an incomplete perspective which may result in side effects. More critically, as the SDN paradigm evolves, additional fault types are being exposed. Therefore, comprehensive reviews and constant improvements are required to remain on the leading edge of SDN fault management. In this paper, we present the first comprehensive and systematic survey of SDN faults and related management solutions identified through advancements in both the research community and industry. We apply a systematic classification of SDN faults, compare and analyze existing SDN fault management solutions in the literature, and conduct a gap analysis between solutions developed in an academic research context and practical deployments. The current challenges and emerging trends are also noted as potential future research directions. This paper aims to provide academic researchers and industrial engineers with a comprehensive survey with the hope of advancing SDN and inspiring new solutions.

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.

Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones and various wearable devices. To protect the privacy of users, various user authentication mechanisms have been proposed. In particular, biometrics has been widely used for multi-factor authentication. However, biometrics-based authentication mechanisms usually require costly sensors deployed on devices, and rely on explicit user input and Internet connection for performing user authentication. In this article, we propose a system, called RiskCog, which can authenticate the ownership of mobile devices unobtrusively and in a real time manner by adopting a learning-based approach. Unlike previous studies on user authentication, for cross-platform deployment, maximum user privacy protection, and unobtrusive authentication, RiskCog only relies on those widely available and privacy-insensitive motion sensors to capture the data related to the users' daily device usage. It requires no users' explicit input and has no requirement on the users' motion state or the device placement. RiskCog is also usable in the environment without Internet access by performing offline user identity verification. We conduct comprehensive experiments on smartphones and smartwatches, which show that RiskCog can authenticate device users rapidly and with high accuracy.

In order to simplify the complexity and reduce the cost of the microphone array, this paper proposes a dual-microphone based sound localization and speech enhancement algorithm. Based on the time delay estimation of the signal received by the dual microphones, this paper combines energy difference estimation and controllable beam response power to realize the 3D coordinate calculation of the acoustic source and dual-microphone sound localization. Based on the azimuth angle of the acoustic source and the analysis of the independent quantity of the speech signal, the separation of the speaker signal of the acoustic source is realized. On this basis, post-wiener filtering is used to amplify and suppress the voice signal of the speaker, which can help to achieve speech enhancement. Experimental results show that the dual-microphone sound localization algorithm proposed in this paper can accurately identify the sound location, and the speech enhancement algorithm is more robust and adaptable than the original algorithm.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.