Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video surveillance (CVS) has become a hotly discussed topic, especially when facing the requirement of real-time analysis in smart applications. Object detection usually plays an important role for environment monitoring and activity tracking in surveillance system. The emerging edge-cloud computing paradigm provides us an opportunity to deal with the continuously generated huge amount of surveillance data in an on-site manner across IoT systems. However, the detection performance is still far away from satisfactions due to the complex surveilling environment. In this study, we focus on the multitarget detection for real-time surveillance in smart IoT systems. A newly designed deep neural network model called A-YONet, which is constructed by combining the advantages of YOLO and MTCNN, is proposed to be deployed in an end-edge-cloud surveillance system, in order to realize the lightweight training and feature learning with limited computing sources. An intelligent detection algorithm is then developed based on a preadjusting scheme of anchor box and a multilevel feature fusion mechanism. Experiments and evaluations using two data sets, including one public data set and one homemade data set obtained in a real surveillance system, demonstrate the effectiveness of our proposed method in enhancing training efficiency and detection precision, especially for multitarget detection in smart IoT application developments.

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

In the realm of federated learning (FL), the conventional dual-layered architecture, comprising a central parameter server and peripheral devices, often encounters challenges due to its significant reliance on the central server for communication and security. This dependence becomes particularly problematic in scenarios involving potential malfunctions of devices and servers. While existing device-edge-cloud hierarchical FL (HFL) models alleviate some dependence on central servers and reduce communication overheads, they primarily focus on load balancing within edge computing networks and fall short of achieving complete decentralization and edge-centric model aggregation. Addressing these limitations, we introduce the multicenter HFL (MCHFL) framework. This innovative framework replaces the traditional single central server architecture with a distributed network of robust global aggregation centers located at the edge, inherently enhancing fault tolerance crucial for maintaining operational integrity amidst edge network disruptions. Our comprehensive experiments with the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate the MCHFL’s superior performance. Notably, even under high paralysis ratios of up to 50%, the MCHFL maintains high accuracy levels, with maximum accuracy reductions of only 2.60%, 5.12%, and 16.73% on these datasets, respectively. This performance significantly surpasses the notable accuracy declines observed in traditional single-center models under similar conditions. To the best of our knowledge, the MCHFL is the first edge multicenter FL framework with theoretical underpinnings. Our extensive experimental results across various datasets validate the MCHFL’s effectiveness, showcasing its higher accuracy, faster convergence speed, and stronger robustness compared to single-center models, thereby establishing it as a pioneering paradigm in edge multicenter FL.

Internet of Things (IoT) is one of the rapidly developing technologies today that attract huge real-world applications. However, the reality is that IoT is easily vulnerable to numerous types of cyberattacks and anomalies. Detecting them is becoming increasingly challenging day by day due to limitations with IoT devices and threat intelligence. Particularly, one of the most challenging problems is to detect the existence of malicious adversaries that continuously adapt or conceal their behaviors in IoT to hide their actions and to make the IoT security protocol ineffective. In this article, we study this problem at the IoT device level that can be a great idea to avoid potential attacks. We present AntiConcealer , an edge-aided IoT framework, and propose an edge artificial intelligence-enabled approach (EdgeAI) for detecting adversary concealed behaviors in the IoT. We first develop an adversary behavior model and use this to identify mid-attack temporal patterns by learning the multivariate Hawkes process (MHP), a kind of point process as a random and finite series of events (e.g., behaviors) controlled by a probabilistic model. Naturally, learning MHP processed on EdgeAI reveals the influence of the concealed behaviors of adversaries in the IoT. These concealed behaviors are then grouped using a nonnegative weighted influence matrix. To observe the performance of the AntiConcealer framework through evaluation, we employ honeypots integrated with edge servers and verify the usability and reliability of adversary behavioral identification.

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.

Tracking the frequency of power systems accurately and promptly is the cornerstone of power industry applications. The excellence of windowed interpolation discrete Fourier transform (DFT)–based methods has been proved in frequency estimation by suppressing both short- and long-range spectral leakage. However, due to the nonnegligible image component, their behavior deteriorates dramatically when short time windows are adopted. To this purpose, a novel frequency estimator based on two-point DFT and Hanning window is proposed for estimating distorted power signals. The method starts with an accurate spectral model that fully considers the effects of the image component. An iterative procedure is then designed to approach the minimum estimation error by using the golden section search. Simulation and experimental results demonstrate that the method offers fast and accurate estimation within a short time window (even as short as 0.5–1.5 cycles).