2026 September - 33 articles
For Internet of Underwater Things (IoUT)−oriented condition monitoring of underwater propulsion systems, continuous visual observations can provide rich information on the spatial distribution and temporal evolution of cavitation, while the dir...
Early-exit deep neural networks (DNNs) can reduce edge-inference latency, but abrupt variations in wireless and computing resources can destabilize split-inference policies. This paper proposes a meta-learning-driven adaptive control framework for jo...
This paper presents a maritime Internet of Things anti-theft architecture based on an ESP32 onboard node, local motion analysis, and LoRa mesh communication. The proposed detection pipeline uses a one-dimensional convolutional autoencoder trained on...
Internet of Things (IoT) botnet detection requires high predictive performance, low client-side resource demands, and limited exposure of raw traffic data. This paper presents a traffic-constrained multi-client split-learning (TC-SL) intrusion-detect...
Automated surface defect detection in hot-rolled steel is a prerequisite for real-time quality control, yet most deployed inspection systems operate in isolation from Industrial Internet of Things (IIoT) infrastructure. This paper reports a laborator...
Passive acoustic monitoring of ocean surface wind speed and rainfall faces severe degradation in shallow waters due to multipath propagation and non-meteorological noise interference. To address these challenges within the Internet of Underwater Thin...
The convergence of Large Language Models (LLMs) with the Internet of Things (IoT) is driving a transformative shift toward a continuous, context-aware smart healthcare ecosystem. Due to its novelty, existing research in this domain remains fragmented...
Task offloading in vehicular edge networks must satisfy strict latency limits. At the same time, it must resist security threats such as replay attacks. Existing offloading models treat cryptographic settings as fixed, separate from the scheduling de...
Patient recovery prediction and hospital length of stay estimation remain critical challenges in healthcare resource allocation and clinical decision-making. Inaccurate discharge planning drives substantial avoidable hospital costs, while existing ma...
Environmental conditions such as inadequate lighting, excessive noise, and poor indoor air quality can negatively affect students’ health, comfort, and academic performance. Although recent Internet of Things (IoT) technologies have enabled low...
Transformer lifecycle data across organizations are typically vertically partitioned among material suppliers, manufacturers, logistics service providers, testing agencies, and operation and maintenance units. This study presents a reproducible multi...
Continuous monitoring of water quality is essential for supporting environmental management and enabling timely decision-making in wastewater treatment facilities. Although numerous Internet of Things (IoT) solutions have been proposed for environmen...
This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management. It examines the role of key enabling technologies, including the Internet...
While federated learning offers a decentralized approach to model training, ensuring the integrity of the information from each IoT client remains a challenge. This work delves into the dynamics of multi-stage federated learning, its susceptibility t...
Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffi...
The Multi-Node Allocation (MNA) problem in Internet of Things (IoT) networks arises when application requirements exceed the capacity of a single node, requiring job distribution across multiple devices. This problem is challenging in large-scale het...
Cardiovascular diseases are among the leading causes of mortality worldwide, making continuous electrocardiographic (ECG) monitoring essential for the early detection and follow-up of cardiac abnormalities. This study presents an Internet of Things (...
The Message Queuing Telemetry Transport (MQTT) protocol has become one of the most widely used messaging protocols for IoT and Industrial Internet of Things (IIoT) applications because of its lightweight publish–subscribe architecture. However,...
Cyberattacks represent a serious threat for the security of network-based systems and are responsible for large economic losses every year. In this context, physical unclonable function (PUF)-based authentication can provide access to the network res...
The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and...
This paper presents Open Source Integration for Internet of Things (OSI4IOT), an open-source software platform designed to support the integration of sensor-driven Internet of Things (IoT), Asset Information Modelling (AIM), Geographical Information...
IoT gateway–cloud systems support large-scale sensing, monitoring, and control applications, but must operate under finite buffering, dynamic traffic demands, and service interruptions caused by gateway and cloud failures. These challenges can...
Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within...
Mining operations generate continuous sensor data across heterogeneous repositories with no unified access layer. Existing integration platforms either require centralizing data into new infrastructure or demand extensive pipeline reconfiguration whe...
Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work, to our knowledge...
Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. T...
Federated learning (FL) is a promising paradigm for privacy-preserving IoT intrusion detection, but its effectiveness can be substantially degraded by the combination of heterogeneous non-IID client distributions and severe multi-class imbalance. Und...
Narrowband Internet of Things (NB-IoT) is a 3GPP-standardised low-power wide-area network (LPWAN) technology designed for massive machine-type communications in challenging propagation environments. Despite its growing deployment, empirical channel d...
The Social Internet of Things (SIoT) has emerged as a promising paradigm for addressing interoperability, adaptability, and intelligent collaboration challenges in smart city environments. However, existing solutions often provide only partial suppor...
The rapid proliferation of Internet of Things (IoT) devices within 5G-enabled smart city environments has introduced unprecedented challenges in communication protocol management across heterogeneous network architectures. With connected IoT devices...
Distributed Denial of Service (DDoS) attacks pose significant threats to Industrial Internet of Things (IIoT) environments, exacerbated by the resource constraints of IoT devices and the disruptive impact of such attacks. Conventional detection and p...
A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum ut...
The application of Internet of Things-based ecosystems is growing rapidly. Cyber attacks are also increasing at a similar pace. Intrusion detection using deep learning is getting harder as these devices lack enough resources for a large Intrusion Det...
