Internet of Things最新文献

{"title":"Effects of light variations on drone’s visual positioning","authors":"Che-Cheng Chang,&nbsp;Po-Ting Wu,&nbsp;Bo-Yu Liu,&nbsp;Bo-Ren Chen","doi":"10.1016/j.iot.2025.101578","DOIUrl":"10.1016/j.iot.2025.101578","url":null,"abstract":"<div><div>Positioning systems and algorithms play a crucial role in drone applications. Although Global Positioning Systems (GPS) are the most widely used method for drone localization, they are not always reliable and accurate in some scenarios. A recent study explores the visual-based positioning method, using Convolutional Neural Networks (CNNs) to match geometric features for drone positioning. The authors use an orthophotomap obtained from an actual drone to evaluate their algorithm. This can reduce the gap between research and practical operation. However, the approach overlooks the impact of lighting variations on positioning performance, i.e., brightness and color temperature. To address this limitation, we propose a novel CNN architecture to handle lighting variations. Our method improves reliability, accuracy, and computational complexity under varying lighting conditions by incorporating several critical components into the network. Remarkably, our architecture has only 51.35% trainable parameters and 83.97% floating point operations (FLOPs) of the existing one. Still, we can exceed it by 3.73% while not considering light variations and average 2.36% while considering light variations. The experimental results, also derived from an orthophotomap obtained via an actual drone, demonstrate that our approach effectively mitigates the challenges induced by lighting changes, ensuring reliable and accurate drone localization.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101578"},"PeriodicalIF":6.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proposal for a security and privacy enhancement system for private smart environments","authors":"Sonia Solera-Cotanilla ,&nbsp;Manuel Álvarez-Campana ,&nbsp;Carmen Sánchez-Zas ,&nbsp;Mario Vega-Barbas","doi":"10.1016/j.iot.2025.101585","DOIUrl":"10.1016/j.iot.2025.101585","url":null,"abstract":"<div><div>Far from being considered a consolidated and regulated paradigm, the Internet of Things has multiple unaddressed challenges that open the way to unresolved security and privacy issues. The reality is that just as technology has evolved, so have attacks on devices, which are becoming increasingly sophisticated and complicated to prevent and detect. This problem is of particular concern in private environments where sensitive data are handled and which, on many occasions, require an early response to conditions of uncertainty. In this sense, this paper contributes to improving the security and privacy of connected devices in private environments. To this end, we propose a system for managing the security and privacy of connected devices that is adaptable to the environment’s requirements. This system, integrated in the router, consists of a set of components that address the problem through the tasks of monitoring and data acquisition, information storage, data analysis, event processing, and data visualisation. Finally, a set of mechanisms is proposed to further automate the secure integration and continuous monitoring of devices in order to make processes more secure and efficient. Thus, these mechanisms, which can be integrated into the proposed system, provide the environment with real-time management capabilities of the devices and notification of alerts detected in the home network, with the sole purpose of keeping the environment secure against possible threats and attacks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101585"},"PeriodicalIF":6.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-based federated learning with homomorphic encryption for privacy-preserving healthcare data sharing","authors":"Muhammad Firdaus ,&nbsp;Harashta Tatimma Larasati ,&nbsp;Kyung Hyune-Rhee","doi":"10.1016/j.iot.2025.101579","DOIUrl":"10.1016/j.iot.2025.101579","url":null,"abstract":"<div><div>Healthcare data is often fragmented across various institutions due to its highly sensitive and private nature. In this sense, hospitals and clinics maintain electronic health records (EHRs) independently; hence, valuable data is siloed within individual organizations, preventing comprehensive analysis that could benefit from diverse data sources. Federated learning (FL) addresses these challenges by enabling the training of a shared global model using data distributed across multiple institutions without moving the data from its source. By leveraging FL, healthcare institutions can combine their data assets to improve predictive analytics, personalized medicine, and overall healthcare outcomes, ultimately benefiting patients and the healthcare system. However, the current FL model with a central server presents several challenges within healthcare, including the risk of malicious attacks, regulatory compliance, and privacy vulnerabilities. To overcome these issues, this paper introduces the FL framework with blockchain and homomorphic encryption (HE). Our framework aims to minimize the role of the central server, enable collaborative model training across healthcare organizations, and enhance data security and privacy. In this sense, blockchain ensures the integrity and transparency of the process, while homomorphic encryption ensures that the data remains private. This framework can potentially enable institutions to enrich medical knowledge while securely keeping patient data collaboratively and facilitating healthcare analytics in practical settings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101579"},"PeriodicalIF":6.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System Development Life-Cycle Assisted Digital Twin Development Model for Smart Micro-grids","authors":"Erol Özkan ,&nbsp;İbrahim Kök ,&nbsp;Suat Özdemi̇r","doi":"10.1016/j.iot.2025.101580","DOIUrl":"10.1016/j.iot.2025.101580","url":null,"abstract":"<div><div>Digital Twin (DT) is an innovative technology that creates virtual representations of assets, processes, and systems, enabling real-time remote monitoring and control, risk assessment, and intelligent planning. Recently, DT has shown potential for application in various fields, including manufacturing, aerospace, healthcare, 6G networks, transport systems, and smart cities. However, it faces challenges such as ensuring data integrity and quality, managing large volumes of big data, dealing with integration complexities, addressing security and privacy concerns, and mitigating high development costs. Moreover, the lack of standardized approaches for DT development further compounds these challenges. This paper presents a System Development Life Cycle (SDLC)-based DT model designed to address the lack of standardization and provide a structured framework for DT development. The proposed model is demonstrated through a case study on grid-connected micro-grids, where each component is deployed as a micro-service within a cloud environment. Furthermore, open-source forecasting tools and data analytics methods are integrated into the DT to predict future trends and analyze performance. By applying the proposed SDLC-assisted development model to DT development, efficiently utilizing cloud resources, and seamlessly managing micro-grid DT operations, key challenges related to cost, scalability, integration, regulatory compliance, and standardization are successfully addressed.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101580"},"PeriodicalIF":6.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADRL: A reconfigurable energy-efficient transmission policy for mobile LoRa devices based on reinforcement learning","authors":"Laura Acosta-Garcia ,&nbsp;Juan Aznar-Poveda ,&nbsp;Antonio Javier Garcia-Sanchez ,&nbsp;Jakob Hollenstein ,&nbsp;Joan Garcia-Haro ,&nbsp;Thomas Fahringer","doi":"10.1016/j.iot.2025.101577","DOIUrl":"10.1016/j.iot.2025.101577","url":null,"abstract":"<div><div>LoRa is a wireless communication technology for low-power and long-range Internet of Things (IoT) networks. Although it was not initially designed for mobile IoT devices, its applicability has quickly expanded to support mobility. LoRaWAN, its network protocol, includes an Adaptive Data Rate (ADR) mechanism to minimize energy consumption while optimizing data rates and Time-on-Air values. However, ADR struggles to adapt effectively in highly dynamic and unpredictable environments, leading to slow convergence and performance degradation. These challenges are particularly evident in scenarios with frequent changes in signal conditions, such as urban areas with non-line-of-sight (NLoS) or environments with rapid channel variations. To address these limitations, we introduce <span>ADRL</span>, a novel transmission mechanism that proactively selects the most suitable transmission configuration of LoRa devices under dynamic and variable conditions. To this end, the proposed method (i) forecasts the received signal strength of end devices using a lightweight k-nearest neighbors (KNN)-based approximation, and (ii) employs deep reinforcement learning to create a reconfigurable controller on the network server side for selecting the transmission parameters that minimize energy consumption and ensure a given probability of successful packet decoding. Moreover, we leverage duty-cycle information to maximize the delivery ratio and comply with the associated regulations. We compare our method to state-of-the-art works using a realistic simulator in various urban environments. The results show that <span>ADRL</span> effectively balances energy consumption and performance requirements, reducing average energy expenditure by up to 15% and increasing the packet delivery ratio by up to 57%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101577"},"PeriodicalIF":6.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-learning adaptive power management scheme for energy-efficient IoT-MEC systems using soft actor-critic algorithm","authors":"Amir Masoud Rahmani ,&nbsp;Amir Haider ,&nbsp;Komeil Moghaddasi ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Khursheed Aurangzeb ,&nbsp;Zhe Liu ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.iot.2025.101587","DOIUrl":"10.1016/j.iot.2025.101587","url":null,"abstract":"<div><div>The rapid increase of Internet of Things (IoT) devices in Mobile Edge Computing (MEC) environments requires effective energy management to ensure device operation and enhance network efficiency. IoT-MEC systems face challenges such as varying task loads, dynamic environmental conditions, and limited energy resources. These factors make it challenging to design adaptive and efficient energy strategies. Traditional methods, such as static scheduling and centralized control strategies, struggle to adapt to real-time fluctuations in task loads and network conditions, resulting in inefficient energy use, higher latency, and a lack of flexibility to respond to these demands in real-time. This paper proposes a self-learning power management model using the Soft Actor-Critic (SAC) algorithm. It is deployed on IoT devices to enable localized and context-aware power management. Our model includes modules for energy monitoring, adaptive task prioritization, and a self-adjusting reinforcement-learning mechanism, which dynamically fine-tunes energy policies based on real-time device conditions, allowing each device to optimize power use independently without heavy dependence on centralized control. MEC nodes gather data on battery health, load, and network conditions to support decentralized policy adjustments. Connected devices in simulated smart homes served as the primary context for evaluation. Experimental results show that our model achieves a 45 % reduction in energy consumption in smart home environments, a 49 % improvement in battery life (compared to baseline-like Local Computing), and high adaptability in diverse scenarios compared with other methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101587"},"PeriodicalIF":6.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Hyperdimensional Computing for hierarchical and distributed quality monitoring in smart manufacturing","authors":"Zhiling Chen,&nbsp;Danny Hoang,&nbsp;Fardin Jalil Piran,&nbsp;Ruimin Chen,&nbsp;Farhad Imani","doi":"10.1016/j.iot.2025.101568","DOIUrl":"10.1016/j.iot.2025.101568","url":null,"abstract":"<div><div>In emerging smart manufacturing, the integration of the Internet of Things (IoT) and edge devices is essential for in-situ sensing, communication, and adaptive learning. Federated Learning (FL) leverages edge-cloud collaboration to preserve data privacy and minimize communication overhead compared to centralized models. However, conventional FL approaches face significant challenges in manufacturing: (1) non-Independent and Identically Distributed (non-IID) data and diverse feature distributions complicate local model training within hierarchical, complex industrial data structures; (2) directly overwriting local models with a global model during updates causes clients to lose critical task-specific information unique to their environments; and (3) transmitting model updates causes communication overhead, limiting scalability. We propose Federated Distributed Hyperdimensional Computing (<span><math><mi>FedDHD</mi></math></span>), an FL framework that employs Hyperdimensional Computing (HDC) to optimize communication for hierarchical manufacturing data. Unlike neural networks, HDC offers robust performance with lower computational demands and inherent resilience to noisy, non-IID data, enabling <span><math><mi>FedDHD</mi></math></span> to naturally handle data heterogeneity and reduce computational burdens on edge devices. <span><math><mi>FedDHD</mi></math></span> integrates a hierarchical graph-based learning model with a node pruning module to alleviate computational load and implements a novel client-cloud update strategy leveraging HDC’s high-dimensional representations to streamline synchronization, thereby minimizing communication costs and improving scalability. We validate <span><math><mi>FedDHD</mi></math></span> through a case study on machining using a Sinumerik edge device, focusing on the geometric quality assessment of two counterbore diameters. <span><math><mi>FedDHD</mi></math></span> achieved an F1-score of 95.3% and demonstrated performance improvements of up to 12.6% over state-of-the-art neural network-based FL methods, highlighting its superior efficiency and scalability in complex industrial settings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101568"},"PeriodicalIF":6.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent multi-sensor fusion and anomaly detection in vehicles via deep learning","authors":"Murat Arda Onsu ,&nbsp;Murat Simsek ,&nbsp;Mark Fobert ,&nbsp;Burak Kantarci","doi":"10.1016/j.iot.2025.101561","DOIUrl":"10.1016/j.iot.2025.101561","url":null,"abstract":"<div><div>Deep learning techniques are predominantly used to identify vehicular events such as harsh cornering, harsh braking, and rapid acceleration by analyzing signal data. However, deploying deep learning models demands high-quality, large-scale data, and the processes of data acquisition, labeling, extraction, and processing are often overlooked in the literature. In this article, we focus on detailed dataset creation, including labeling and feature analysis, alongside the development of AI models. Real-time data collection is conducted on experimental roads using numerous vehicles equipped with AI-enabled edge units. The raw data collected, however, is unsuitable for training deep learning models due to redundant features, noisy attributes, and a lack of labeled anomalous events. To address this, we employ multiple preprocessing and postprocessing techniques to generate high-quality datasets, analyzing the specific impacts of each signal feature on anomalous events. Since real-time collected data lacks labels, a thorough labeling process is required for each data point. An autoencoder-based labeling process is applied to the final dataset, where the autoencoder detects and labels anomalous behaviors based on data timestamps. Following the labeling, a hybrid deep learning model incorporating Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), attention, and Fully Connected Neural Networks (FCDNN) layers is trained and tested for detecting anomalous driving events. The results demonstrate that the proposed method outperforms the state-of-the-art solutions by reaching high accuracy rates: 99.69% for harsh cornering events and 98.24% for rapid acceleration and harsh braking events, with corresponding F1 scores of 90.14% and 81.22%, respectively.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101561"},"PeriodicalIF":6.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustered federated learning enhanced by DAG-based blockchain with adaptive tip selection algorithm","authors":"Xiaofeng Xue ,&nbsp;Haokun Mao ,&nbsp;Qiong Li ,&nbsp;Xin Guan","doi":"10.1016/j.iot.2025.101573","DOIUrl":"10.1016/j.iot.2025.101573","url":null,"abstract":"<div><div>Federated learning (FL) enables machine learning on distributed data while preserving client privacy. However, FL faces challenges such as device heterogeneity, central server vulnerabilities, and non-independent and identically distributed data. To address these challenges, researchers proposed an asynchronous and decentralized clustered FL (CFL) using a directed acyclic graph (DAG)-based blockchain, called specializing DAG FL (SDAGFL). However, SDAGFL consumes high communication and storage resources, posing a substantial burden on devices with limited resources. To overcome these limitations, we propose a novel CFL framework called DAG-CFL. DAG-CFL consists of a server layer with multiple servers implementing DAG-based blockchain and a client layer. Within this framework, we propose an adaptive tip selection algorithm (ATSA) to select the most suitable tip nodes for model aggregation. The analysis indicates that DAG-CFL significantly reduces communication and storage resource consumption on the client side compared with SDAGFL. In addition, the convergence of DAG-CFL and the time and space complexity of ATSA are analyzed to show the effectiveness of DAG-CFL. We evaluate DAG-CFL and ATSA on cluster-wise MNIST and CIFAR-10 datasets. The results show that DAG-CFL achieves comparable performance to the best CFL baseline method while eliminating the need for a predefined number of clusters. Notably, DAG-CFL achieves an 8% increase in accuracy compared with SDAGFL. The experiment results also show the robustness of DAG-CLF in various data distribution shift scenarios and indicate that ATSA can effectively cluster clients with a modularity value of 0.66 for the MNIST dataset and 0.71 for the CIFAR-10 dataset.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101573"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noise-resilient feature selection for accelerometer-based guyed tower monitoring","authors":"Juliane Regina de Oliveira ,&nbsp;German Efrain Casteñeda Jimenez ,&nbsp;Janito Vaqueiro Ferreira ,&nbsp;Larissa Medeiros de Almeida ,&nbsp;Eduardo Rodrigues de Lima ,&nbsp;Lucas Wanner","doi":"10.1016/j.iot.2025.101563","DOIUrl":"10.1016/j.iot.2025.101563","url":null,"abstract":"<div><div>Guyed towers are vulnerable to environmental hazards that can lead to the collapse of transmission lines, jeopardizing essential services. Relaxed cables represent a critical condition that can result in structural failure. A Structure Health Monitor (SHM) is an Internet of Things (IoT) application that relies on an accelerometer to measure cable-stayed vibrations. This data is then inputted into a machine learning algorithm to identify relaxed cables. We incorporated a feature engineering step to enhance machine learning inference and mitigate uncertainty from raw accelerometer channels. However, there are concerns regarding high dimensionality and complexity arising from more irrelevant and correlated features. Additionally, sensors from IoT applications can introduce various types and magnitudes of noise. eXplainable Artificial Intelligence (XAI) approaches enable feature importance ranking and select more relevant and influential features. We experimented with traditional feature selection and XAI approaches to feature importance rankings. Our results show the robustness of features selected by XAI approaches compared to traditional accuracy. The baseline, which includes all features, achieved an accuracy of 96%, while the performance of machine learning algorithms under noise varied between 87% and 98%, closer to the baseline.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101563"},"PeriodicalIF":6.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}