相关论文: In-Network Processing for Low-Latency Industrial A…

Accurately predicting end-to-end network latency is essential for enabling reliable task offloading in real-time edge computing applications. This paper introduces a lightweight latency prediction scheme based on rational modelling that…

Tiny Machine Learning enables real-time, energy-efficient data processing directly on microcontrollers, making it ideal for Internet of Things sensor networks. This paper presents a compact TinyML pipeline for detecting anomalies in…

Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals. Typically, it requires modeling system behavior, which is…

Future sixth-generation (6G) networks are envisioned to support intelligent applications across various vertical scenarios, which have stringent requirements on high-precision sensing as well as ultra-low-latency data processing and…

In the era of Industry 4.0, smart factories have emerged as a paradigm shift, redefining manufacturing with the integration of advanced digital technologies. Central to this transformation is the deployment of 5G networks, offering…

In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and…

Wireless Sensor Networks (WSN) are the backbone of essential monitoring applications, but their deployment in unfavourable conditions increases the risk to data integrity and system reliability. Traditional fault detection methods often…

The rapid growth of the Internet of Things (IoT) has given rise to highly diverse and interconnected ecosystems that are increasingly susceptible to sophisticated cyber threats. Conventional anomaly detection schemes often prioritize…

We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet can efficiently detect silent data corruption originating…

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes…

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors…

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are…

The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally,…

The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is…

While technologies such as Time-Sensitive Networking (TSN) improve deterministic behaviour, real-time functionality, and robustness of Ethernet, future industrial networks aim to be increasingly wireless. While wireless networks facilitate…

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the…

Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware…

Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart…

Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not…

Implementing machine learning algorithms on Internet of things (IoT) devices has become essential for emerging applications, such as autonomous driving, environment monitoring. But the limitations of computation capability and energy…