The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time p...
As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively i...
The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and...
With the increase in the number of surveillance cameras being deployed globally, an important topic is person re-identification (Re-ID), which identifies the same person from multiple different angles and different directions across multiple cameras....
High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the...
Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization o...
The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the con...
Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the...
Industry 4.0 introduced new concepts, technologies, and paradigms, such as Cyber Physical Systems (CPSs), Industrial Internet of Things (IIoT) and, more recently, Artificial Intelligence of Things (AIoT). These paradigms ease the creation of complex...
Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face lim...
It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time,...
The rapid growth of Artificial Intelligence of Things (AIoT) environments in various sectors has introduced major security challenges, as these smart devices can be exploited by malicious users to form Botnets of Things (BoT). Limited computational r...
The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extend...
Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secu...
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, th...
The integration of solar photovoltaic (PV) systems into smart grids necessitates robust, real-time fault detection mechanisms, particularly in resource-constrained environments like the Solar–Hydrogen AIoT microgrid framework at a university. T...
Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for long-term...
Deploying artificial intelligence on edge nodes of Low-Power Wide Area Networks can significantly reduce network transmission volumes, event response latency, and overall network power consumption. However, the edge nodes in LPWAN bear limited comput...
The emerging advances in deep learning and computer vision have enabled traditional cloud-based decision-making through edge computing with artificial intelligent internet of things (AIoT) image sensors (AIoT-IS). As a result, the timeliness and secu...
Artificial intelligence of things (AIoT) has become a pivotal enabler of precision agriculture by supporting real-time, data-driven decision-making at the edge. Deep learning (DL) models are central to this paradigm, offering powerful capabilities fo...
The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural...
The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challen...
Lighting Systems (LSs) play a fundamental role in almost every aspect of human activities. Since the advent of lights, both academia and industry have been engaged in raising the quality of the service offered by these systems. The advent of Light Em...
The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrai...
Smart agricultural systems are continually evolving to provide high-quality planting and defend against threats such as climate change, which necessitate improved adaptation and resource allocation. IoT technology offers a cost-effective approach to...
Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the high computational complexity and high-energy consumption of CNNs trammel their application in hardware accelerators. Computing-in-memory (CIM) is the te...
This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture...
Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presen...
In terms of memory footprint requirement and computing speed, the binary neural networks (BNNs) have great advantages in power-aware deployment applications, such as AIoT edge terminals, wearable and portable devices, etc. However, the networks’ bina...
Event-based sensors provide sparse, motion-centric measurements that can reduce data bandwidth and enable always-on perception on resource-constrained edge devices. This paper presents an event-based machine vision framework for smart-home AIoT that...