Internet of Things最新文献

The booming development of automation in industry has seen robotic arms replace much of manual labor for tasks such as casting, processing, packaging, and gripping on production lines. The Internet of Things (IoT) framework enables machines to transmit data over networks, and combining it with artificial intelligence can create smarter systems with higher operational efficiency and quality. However, artificial intelligence models need to be optimized for different applications. This paper proposes a You Only Look Once–uniform experimental design (YOLO–UED) model for gripping tasks performed by an IoT-based robotic arm. The YOLO–UED model was designed by combining the YOLOv4 model with UED to optimize the model architecture, resulting in improved performance in various applications. Considering the huge expense of computational resources required for visual inspection with robotic arms, pairing each robotic arm with a high-performance computing device would substantially increase costs. This study proposed an IoT framework to transmit the images captured by the robotic arm to a computing server for object recognition. Utilizing the IoT framework helps reduce costs and provides scalability and flexibility in handling computational tasks. The proposed method was found to effectively enhance the model's mean average precision to 95 %. The YOLO–UED model exhibited 7–10 % improvement over the YOLOv4 model in terms of target recognition accuracy. Moreover, the proposed method attained a success rate of 90% in gripping tasks performed on objects placed at various angles.

In this paper, we propose an innovative blockchain deployment mechanism tailored for resource-constrained rate-splitting multiple access (RSMA) Internet of Things (IoT) networks. To address the storage limitations and security concerns inherent in IoT environments, our approach includes several advanced techniques. First, we utilize a Block Allocation Strategy Contract (BASC) to manage storage efficiently. Second, we employ a deep reinforcement learning (DRL) model to dynamically perform block assignment, ensuring optimal storage utilization. To accommodate the resource constraints of IoT devices, we adopt the Smart Byzantine Fault Tolerance (SBFT) consensus mechanism, which offers low latency and energy efficiency. Our framework demonstrates superior performance in storage optimization and reduced running time compared to existing methods, making it well-suited for large-scale IoT networks. Through extensive simulations, we validate the effectiveness of our proposed solution in enhancing security and operational efficiency in RSMA IoT networks.

We developed a two-stage traffic sign recognition system to enhance safety and prevent tragic traffic incidents involving self-driving cars. In the first stage, YOLOv7 was employed as the detection model for identifying 31 types of traffic signs. Input images were set to 640 × 640 pixels to balance speed and accuracy, with high-definition images split into overlapping sub-images of the same size for training. The YOLOv7 model achieved a training accuracy of 99.2 % and demonstrated robustness across various scenes, earning a testing accuracy of 99 % in both YouTube and self-recorded driving videos. In the second stage, extracted road sign images underwent rectification before processing with OCR tools such as EasyOCR and PaddleOCR. Post-processing steps addressed potential confusion, particularly with city/town names. After extensive testing, the system achieved recognition rates of 97.5 % for alphabets and 99.4 % for Chinese characters. This system significantly enhances the ability of self-driving cars to detect and interpret traffic signs, thereby contributing to safer road travel.

Defect detection is crucial in manufacturing processes but traditional AI-based algorithms require large datasets for accurate results. For new or customized products, the number of images with detected defects is limited. Therefore, we developed a few-shot learning approach integrating a prototypical and relation network (PRN), algorithms with meta-learning, and the Artificial Internet of Things (AIoT). For rapid defect detection with IoT sensors, such minimal data are used for a smart manufacturing ecosystem., making it ideal for dynamic production environments. We tested the AIOT-enhanced PRN on two datasets using the following data augmentation methods: random rotation and horizontal translation (RH), random rotation and vertical translation (RV), and horizontal and vertical translation (HV). The developed PRN efficiently learned from minimal data to reduce the occurrence of overfitting issues in the MVTec 3D-AD dataset which are caused by a limited number of defect sample images. When testing the AIOT-enhanced PRN with the NEU-DET dataset, accuracies in 5-way 5-shot settings using RV, RH, 15° rotation, and HV were 100 %. Under Gaussian noise, the AIOT-enhanced PRN showed an accuracy of 100 % in 5-way 5-shot and 5-way 1-shot scenarios using HV. For salt-and-pepper noise, the accuracy of the AIOT-enhanced PRN ranged from 98.49 to 99.04 %. The developed AIOT-enhanced PRN improved defect detection accuracy and real-time monitoring capability with minimal data. The developed AIOT-enhanced PRN can be used for efficient and flexible product quality control in Industry 4.0.

During the digital age of healthcare, it is crucial to utilize medical data scattered across different healthcare institutions to improve diagnostic precision and customize treatment strategies. A common solution to achieve this is establishing an authorization service that facilitates secure sharing of medical data and promotes interoperability among various healthcare institutions. However, there is a risk of a single point of failure because the majority of authorization systems in use rely on a central trusted service. This paper proposes DAMFSD, a decentralized authorization model with flexible and secure permissions delegation for medical data sharing. Specifically, patients can transfer their permissions to reliable institutions or individuals for flexible management and delegation while they retain control and monitor their permissions. We use cryptographic techniques for secure and fine-grained delegation and smart contracts to enable decentralized and flexible delegation. Finally, we performed a security analysis to demonstrate DAMFSD’s feasibility and conducted a performance evaluation on the permissioned blockchain to show its applicability.

This study explores the current challenges and future directions of Internet of Things (IoT) in supply chains, focusing on the drivers and barriers to its adoption. It starts with a review of 913 documents from the Web of Science, spanning 2009 – 2023, and narrows down to 408 relevant publications. Employing bibliometric analysis—descriptive, trend topic, conceptual, and network analyses—the research addresses the challenges at the intersection of IoT and supply chain. Findings highlight a surge in research from 2018, with worldwide contributions. The study identifies the 20 most active countries, top 10 journals, and key papers in the field. The result reveals a transition from studies predominantly focused on enhancing efficiency in the supply chain using IoT to an increased emphasis on resilience, particularly due to global disruptions like COVID-19, while also considering sustainability and digital transformation. Integrating machine learning and IoT to predict future conditions in supply chains marks also a novel approach. Based on the network analysis several critical challenges, including issues of security, privacy, interoperability, standardization, scalability, and energy efficiency, are identified as obstacles to effective IoT integration. The research also points to blockchain technology as a promising solution for addressing these challenges, facilitating decentralized trust, and enhancing cybersecurity. Moreover, the study emphasizes IoT's capacity for real-time tracking of logistics, production, and agri-food supply chains. A SWOT analysis outlines critical factors for integrating IoT in supply chain management, providing policy recommendations for industry practitioners and a framework for further investigation into IoT's potential and challenges in supply chains.

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the ”Adaptation-Based Hybrid Evolutionary Algorithm,” this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm’s control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.

Integrating smart grids in smart cities is pivotal for enhancing urban sustainability and efficiency. Smart grids enable bidirectional communication between consumers and utilities, enabling real-time monitoring and management of electricity flows. This integration yields benefits such as improved energy efficiency, incorporation of renewable sources, and informed decision-making for city planners. At the city scale, forecasting electricity consumption is crucial for effective resource planning and infrastructure development. This study proposes using a time-series dense encoder model for short-term and long-term forecasting at the city level, showing its superior performance compared to traditional approaches like recurrent neural networks and statistical methods. Hyperparameters are optimized using the non-dominated sorting genetic algorithm. The model’s efficacy is demonstrated on a six-year dataset, highlighting its potential to significantly improve electricity consumption forecasting and enhance urban energy system efficiency.

The research here presented aims to analyze the global impact of e-governance in the European Union. It focuses on the long-term development of the European Union following the spillover effect described by Neofunctionalism integration theory. Therefore, it explores the potential of e-government fostering the European integration to a new level thanks to the new digital possibilities. New information and communication technologies can make a significant contribution to the achievement of good governance goals. This EU “e-governance” can make public management more efficient and more effective and attract the loyalty of the participants to the European project. This research outlines the main contributions of e-governance in Europe: improving government processes (e-administration); connecting citizens (e-citizens and e-services); and building external interactions (e-society). Case studies are used to show that e-governance is a current, not just future, reality for developing countries and the European Union. E-governance requires a concrete roadmap for its development and this chapter focuses on the main steps required to implement an effective e-governance within the European Union.

The emergence of the Internet of Things (IoT) has posed a new challenge for forensic investigators, who find themselves carrying out examinations in a very heterogeneous and novel scenario. Aspects such as the high number of devices, the unlikelihood of having physical access to them, the short lifetime of the data, or the difficulty of acquiring it, demand changes in some of the key processes of forensic investigations. In this regard, the identification, acquisition, and analysis phases call for an IoT-centred approach that can fulfil the requirements of the environment. Due to the interoperability of the IoT, and the way in which the data is handled and exchanged, the network traffic becomes a very useful source of evidence. In view of this, this paper presents an automatic procedure for identifying, analysing, and acquiring IoT network traffic and using it as a basis for forensic examinations by employing an edge node capable of performing real-time traffic monitoring and analysis on the most popular IoT protocols. Furthermore, by pairing it with an Intrusion Detection System (IDS) based on Machine Learning (ML) algorithms, the proposal is capable of following a proactive approach, detecting threats and taking the corresponding measures to assure the correct initiation of a forensic process.