Internet of Things最新文献

{"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}

{"title":"Reliable PID dual-rate controller based on LoRaWAN for long-range distributed systems","authors":"Salvatore Dello Iacono,&nbsp;Alessandro Depari,&nbsp;Paolo Ferrari,&nbsp;Alessandra Flammini,&nbsp;Stefano Rinaldi,&nbsp;Emiliano Sisinni","doi":"10.1016/j.iot.2025.101570","DOIUrl":"10.1016/j.iot.2025.101570","url":null,"abstract":"<div><div>With the advances in digital communication systems, Network Control Systems (NSC) appeared as a feasible control solution for automation tasks. More recently, the exploitation of ubiquitous Internet connectivity for implementing a NCS has been suggested, paving the way for the Industrial Internet-of-Things paradigm. In particular, moving towards the Control as a Service (CaaS) would allow to benefit from the adaptability and scalability offered by cloud computing. Unfortunately, the needs of automation systems are very different from those dictated by the office and enterprise scenarios. The performance and availability of communication infrastructure used for connecting with the cloud can severely affect the control strategy effectiveness. This study assesses the implementation of a dual-rate controller, enhanced by an appropriate predictor, to facilitate NCS utilizing a potentially unstable LoRaWAN network. The selection of LoRaWAN, a type of Low Power Wide Area Network, is due to its extensive utilization in academic and industrial sectors. Moreover, the standard explicitly delineates backend entities, facilitating novel CaaS business models. A case study of a control system composed of a single LoRaWAN end-node device placed in a local plant to be controlled and connected to a single gateway network is employed to simulate the DR-PID control strategy on a second-order time-continuous system. The simulation model incorporates LoRaWAN characteristics, such as node-initiated transactions, and synthesizes measurable parameters, such as up-link and down-link losses. This model has been employed to assess control performance relative to a reference ideal situation in which communication losses are absent. A figure of merit is defined, enabling the assertion of the suggested approach’s superiority over the plain LoRaWAN NCS. Furthermore, the quasi-orthogonality of time and frequency superposed frames with varying Spreading Factors (SF) is proposed as an alternate method to leverage the enhanced noise immunity provided by elevated SF values.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101570"},"PeriodicalIF":6.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686242","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":"Gateway configuration in 2.4 GHz LoRa networks","authors":"Dimitrios Zorbas ,&nbsp;Luigi Di Puglia Pugliese ,&nbsp;Ruana Saduakhas ,&nbsp;Francesca Guerriero","doi":"10.1016/j.iot.2025.101567","DOIUrl":"10.1016/j.iot.2025.101567","url":null,"abstract":"<div><div>The LoRa radio technology has been recently expanded into the 2.4 GHz spectrum, which offers advantages such as increased bandwidth and global regulation compatibility. 2.4 GHz LoRa promises higher data rates and throughput, crucial for data-intensive industrial applications. It maintains key features such as long-range communication and low power consumption, making it suitable for various industrial Internet of Things (IoT) applications. However, 2.4 GHz gateways face challenges due to their design limitations that require only one Spreading Factor (SF) setting and channel to be assigned to each of the four available transceivers. This design limits the number of available options that the end-devices (EDs) can have and leads to resource allocation problems. To this extent, this paper presents a gateway configuration problem for 2.4 GHz LoRa gateways, mainly aiming at providing a trade-off between energy consumption and fairness among end-devices. A bi-objective optimization problem is introduced, which is solved by considering a convex combination of two objective functions. A <span><math><mi>λ</mi></math></span> <span><math><mrow><mo>∈</mo><mrow><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></mrow></math></span> coefficient is employed to discover a Pareto optimal solution between the two objectives. Due to the high computational complexity of the integer linear programming (ILP) approach, two practical heuristics with lower computation costs are also proposed. Simulation results show that the proposed approaches exhibit better fairness and packet reception ratio (PRR) compared to the static configuration of gateways. Moreover, the results reveal that <span><math><mi>λ</mi></math></span> is capable of providing a trade-off between fairness and energy efficiency. The findings are confirmed by conducting experiments on a small-scale testbed consisting of 16 ESP32 devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101567"},"PeriodicalIF":6.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636671","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":"A robust IoT architecture for smart inverters in microgrids using hybrid deep learning and signal processing against adversarial attacks","authors":"Mahmoud Elsisi ,&nbsp;Shimaa Bergies","doi":"10.1016/j.iot.2025.101576","DOIUrl":"10.1016/j.iot.2025.101576","url":null,"abstract":"<div><div>The increasing autonomy and deployment of cyber-physical systems, particularly power electronics-based inverters within microgrids, has heightened their vulnerability to cyber threats, such as False Data Injection (FDI) and adversarial attacks, which can compromise the integrity of data exchanged across communication networks. To address these security concerns, this paper proposes a new Internet of Things (IoT) architecture that integrates a hybrid approach combining 2-D Convolutional Neural Networks (2-D CNN) with Continuous Wavelet Transform (CWT) for enhanced cyberattack detection. The framework is designed to detect and mitigate adversarial perturbations, focusing on FDI and other attack vectors targeting the communication infrastructure of smart inverters. By transforming raw data into images using CWT, the framework enables efficient statistical feature extraction, enhancing learning accuracy to approximately 98.9 %, outperforming other models. Additionally, it reduces the computational load of signal processing, achieving a processing time of just 0.0548 s. The proposed deep learning model is tested against various levels of cyber perturbations, and its performance is benchmarked against other deep learning and machine learning techniques. The framework is validated using real-time data from a practical distribution system equipped with smart inverters, demonstrating its effectiveness in safeguarding microgrids from cyber threats.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101576"},"PeriodicalIF":6.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697480","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":"Bluetooth low energy indoor positioning: A fingerprinting neural network approach","authors":"Alberto Ferrero-López,&nbsp;Antonio Javier Gallego,&nbsp;Miguel Angel Lozano","doi":"10.1016/j.iot.2025.101565","DOIUrl":"10.1016/j.iot.2025.101565","url":null,"abstract":"<div><div>This study explores the application of neural networks in indoor positioning using BLE (Bluetooth Low Energy) and the Fingerprinting location technique. The methodology involves two main phases: the capture and filtering process, where received BLE signals are smoothed and combined into fingerprint vectors, and the subsequent location prediction phase, which compares the position estimation from eight neural network designs and the classical trilateration method. We conduct a performance comparative analysis of each prediction method and study the optimal parameter values for the capturing and filtering processes. The research underscores the limitations of training metrics in reflecting real-world performance, emphasizing the importance of testing models on actual trajectories. Results indicate that regression neural networks outperform classification ones, and a complex dense neural network model proves most versatile and stable across testing scenarios. Our approach achieves a mean error of 1.9 meters, surpassing existing accuracies of 3.7 meters for trilateration and 3.1 meters for state-of-the-art neural network designs, thus holding promise for significantly improving indoor positioning accuracy with practical implications across various domains.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101565"},"PeriodicalIF":6.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628386","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":"Secure UAV routing with Gannet Optimization and Shepard Networks","authors":"R Yuvaraj ,&nbsp;Velliangiri Sarveshwaran","doi":"10.1016/j.iot.2025.101575","DOIUrl":"10.1016/j.iot.2025.101575","url":null,"abstract":"<div><div>In recent times, Unmanned Aerial Vehicle (UAV) networks have been extensively employed in civilian and military scenarios. However, they are also highly susceptible to threats from adversaries owing to its distributed nature. To ensure reliable and secure functioning of smaller drones, designing a robust network architecture and applying tailored privacy as well as security mechanisms is important. This research presents a Gannet Weaving Optimization Algorithm based Adversarial Shepard Convolutional Spinal Network (GWOA+Adversarial ShCSpinalNet) for efficient routing and malicious detection in UAV. Initially, the UAV network is simulated, and then, routing is accomplished utilizing the Gannet Weaving Optimization Algorithm (GWOA) by considering the multi-objectives. The GWAO is designed by incorporating Gannet Optimization Algorithm (GOA) with Carpet Weaving Optimization (CWO). Here, energy prediction is accomplished by a Dilated Residual Network (DRN). Thereafter, data communication is performed by monitoring agents. Then, malicious detection is carried out employing Adversarial ShCSpinalNet by a decision-making agent, wherein packet delivery, round trip time, signal strength count of incoming packets and size of packet are considered as attributes. Moreover, Adversarial ShCSpinalNet is introduced by combining Shepard Convolutional Neural Network (ShCNN) and SpinalNet with an adversarial loss function. Thereafter, attack mitigation is conducted by a defensive agent. The GWOA+Adversarial ShCSpinalNet attained a maximal detection rate of 94.827 %, energy of 44.755J and Packet Delivery Ratio (PDR) of 76.446 % as well as a minimal delay of 0.553ms.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101575"},"PeriodicalIF":6.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628385","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}