Internet of Things最新文献

{"title":"Environmental noise monitoring using distributed hierarchical wireless acoustic sensor network","authors":"Bo Peng,&nbsp;Kevin I-Kai Wang,&nbsp;Waleed H. Abdulla","doi":"10.1016/j.iot.2024.101373","DOIUrl":"10.1016/j.iot.2024.101373","url":null,"abstract":"<div><div>Acoustic noise pollution is one of many problems people face as cities grow. Long-term noise exposure can result in a series of physical and mental health diseases that are highly harmful to foetuses and newborns. Hence, many IoT-based wireless sensor network systems have been proposed for automated monitoring for long-term operation. However, these systems suffer from weaknesses in functionality, power consumption, costs, and scalability, which hinder large-scale deployment. In this study, we propose a distributed hierarchical wireless acoustic sensor network for environmental noise monitoring to do sound classification and A-weighted sound-pressure-level measurement to address the shortcomings of existing systems. A series of tests and comparisons are performed in diagnosing the performance with respect to recording continuity, packet loss, recording quality, accuracy on A-weighted sound pressure level calculations, and costs. Results show that this proposed network structure is feasible as a part of hardware implementation in a large-scale, low-cost, and high-scalable environmental noise monitoring system to classify sound.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101373"},"PeriodicalIF":6.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003147/pdfft?md5=a50fda5582490254b77711226f7044b5&pid=1-s2.0-S2542660524003147-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310613","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":"Quantifying impact: Bibliometric examination of IoT's evolution in sustainable development","authors":"Marian Stan ,&nbsp;Adriana Dima ,&nbsp;Dag Øivind Madsen ,&nbsp;Cosmin Dobrin","doi":"10.1016/j.iot.2024.101370","DOIUrl":"10.1016/j.iot.2024.101370","url":null,"abstract":"<div><div>The integration of Internet of Things (IoT) and sustainable development (SD) presents innovative solutions to global challenges, reflecting their pivotal roles in shaping future socio-economic and environmental landscapes. This bibliometric study explores the developing research domain of IoT and SD, focusing on their intersection. The extensive literature review section investigates and critically analyses most relevant papers from Web of Science (WoS) database that explore the IoT and SD domain, highlighting their main findings. Based on a WoS dataset of 908 articles from WoS, bibliometric techniques are applied to identify influential topics, scientific production evolution, key contributors, and thematic evolution. Authors like Singh, Gehlot, Akram, and Bibri have substantial publication records in IoT and SD. China leads in article contributions, followed by India, the USA, Spain, and the UK. The study maps the thematic evolution, and identifies emerging themes focusing on management, big data analytics, and environmental aspects like water and food sustainability. Future research directions should reside in interdisciplinary studies that integrate technology into sustainable practices and focus on energy consumption, safety or supply chain management with an emphasis on empirical assessments to understand their real-world impact.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101370"},"PeriodicalIF":6.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003111/pdfft?md5=30d7d6a0a3dd0b212de1a69f084c6183&pid=1-s2.0-S2542660524003111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310611","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":"An IoT-based contactless neonatal respiratory monitoring system for neonatal care assistance in postpartum center","authors":"Yi-Chun Du ,&nbsp;Po-Fan Chen ,&nbsp;Wei-Siang Ciou ,&nbsp;Tsung-Wei Lin ,&nbsp;Tsu-Chi Hsu","doi":"10.1016/j.iot.2024.101371","DOIUrl":"10.1016/j.iot.2024.101371","url":null,"abstract":"<div><div>According to previous studies, one of the major causes of 20 % to 25 % of neonatal deaths is respiratory distress syndrome (RDS). Early identification, progressive monitoring, and treatment and/or management of neonatal RDS can substantially increase the rate of survival in neonates. However, global research indicates frequent shortages and burnout among nursing staff, especially in postpartum units, contributing to the difficulty in early identification of RDS in neonates. Clinicians currently use breathing sounds and frequency as key criteria in the Neonatal Resuscitation Program (NRP) for identifying and treating RDS. In practice, the monitoring of respiratory signal abnormalities relies on sensor patches, which frequently detach from the neonates’ slippery skin, leading to potential skin injuries and unstable signal reception. This paper presents an Internet of Things (IoT)-based contactless neonatal respiratory monitoring system that integrates computer vision (CV), beamforming microphone array (BFMA), and millimeter Wave (mmWave) radar, all connected to a cloud platform. Clinical trials revealed that CV-based neonatal feature identification achieved over 96 % accuracy within 40 cm to 120 cm. The neonatal breathing sound strengthening, utilized CV and BFMA, achieved an average sound-to-noise ratio (SNR) of 5.07 dB, and CV with mmWave radar reduced chest displacement signal error from 0.66 to 0.26 BPM. Additionally, survey results showed that doctors and clinical personnel were satisfied with the system's functionality and usability. This demonstrates the system's ability to assist in monitoring respiratory signals of swaddled neonates and in the early identification of neonatal RDS, with further applications in neonatal care at postpartum centers.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101371"},"PeriodicalIF":6.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003123/pdfft?md5=cbabcee4ddc20698e720a1837b57d21e&pid=1-s2.0-S2542660524003123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310849","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":"EdgeBus: Co-Simulation based resource management for heterogeneous mobile edge computing environments","authors":"Babar Ali ,&nbsp;Muhammed Golec ,&nbsp;Sukhpal Singh Gill ,&nbsp;Huaming Wu ,&nbsp;Felix Cuadrado ,&nbsp;Steve Uhlig","doi":"10.1016/j.iot.2024.101368","DOIUrl":"10.1016/j.iot.2024.101368","url":null,"abstract":"<div><p>Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container orchestration. Intelligent and efficient container placement in Mobile Edge Computing (MEC) is challenging subjected to user mobility, and surplus but heterogeneous computing resources. One solution to constantly altering user location is to relocate containers closer to the user; however, this leads to additional underutilized active nodes and increases migration’s computational overhead. On the contrary, few to no migrations are attributed to higher latency, thus degrading the Quality of Service (QoS). To tackle these challenges, we created a framework named EdgeBus<span><span>¹</span></span>, which enables the co-simulation of container resource management in heterogeneous MEC environments based on Kubernetes. It enables the assessment of the impact of container migrations on resource management, energy, and latency. Further, we propose a mobility and migration cost-aware (MANGO) lightweight scheduler for efficient container management by incorporating migration cost, CPU cores, and memory usage for container scheduling. For user mobility, the Cabspotting dataset is employed, which contains real-world traces of taxi mobility in San Francisco. In the EdgeBus framework, we have created a simulated environment aided with a real-world testbed using Google Kubernetes Engine (GKE) to measure the performance of the MANGO scheduler in comparison to baseline schedulers such as IMPALA-based MobileKube, Latency Greedy, and Binpacking. Finally, extensive experiments have been conducted, which demonstrate the effectiveness of the MANGO in terms of latency and number of migrations.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101368"},"PeriodicalIF":6.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230796","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":"Video streaming on fog and edge computing layers: A systematic mapping study","authors":"André Luiz S. de Moraes ,&nbsp;Douglas D.J. de Macedo ,&nbsp;Laércio Pioli Junior","doi":"10.1016/j.iot.2024.101359","DOIUrl":"10.1016/j.iot.2024.101359","url":null,"abstract":"<div><div>Video streaming has become increasingly dominant in internet traffic and daily applications, significantly influenced by emerging technologies such as autonomous cars, augmented reality, and immersive videos. The computing community has extensively discussed aspects like latency, device power consumption, 5G, and computing. The advent of 6G technology, an emerging communication paradigm beyond existing technologies, promises to revolutionize these areas with enhanced bandwidth, reduced latency, and advanced connectivity features. Fog and Edge Computing environments intensify data generation, control, and analysis at the network edge. Consequently, adopting metrics such as QoE (Quality of Experience) and QoS (Quality of Service) is crucial for developing adaptive streaming services that dynamically adjust video quality based on network conditions. This work systematically maps the literature on video streaming approaches in Fog and Edge Computing that utilize QoS and QoE metrics to evaluate performance in managing Live Streaming and Streaming on Demand. The results highlight the most used metrics and discuss resource management strategies, providing valuable insights for developing new approaches and enhancing existing communication protocols like DASH (Dynamic Adaptive Streaming over HTTP) and HLS (HTTP Live Streaming).</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101359"},"PeriodicalIF":6.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310850","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":"Empirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios","authors":"José García,&nbsp;Jorge Entrena,&nbsp;Álvaro Alesanco","doi":"10.1016/j.iot.2024.101367","DOIUrl":"10.1016/j.iot.2024.101367","url":null,"abstract":"<div><div>This paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101367"},"PeriodicalIF":6.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003081/pdfft?md5=2c59c06adc897db3e81bd94a83f7572e&pid=1-s2.0-S2542660524003081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315585","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":"ASAP: A lightweight authenticated secure association protocol for IEEE 802.15.6 based medical BAN","authors":"Walid I. Khedr ,&nbsp;Aya Salama ,&nbsp;Marwa M. Khashaba ,&nbsp;Osama M. Elkomy","doi":"10.1016/j.iot.2024.101363","DOIUrl":"10.1016/j.iot.2024.101363","url":null,"abstract":"<div><p>Medical Body Area Networks (MBANs), a specialized subset of Wireless Body Area Networks (WBANs), are crucial for enabling medical data collection, processing, and transmission. The IEEE 802.15.6 standard governs these networks but falls short in practical MBAN scenarios. This paper introduces ASAP, a Lightweight Authenticated Secure Association Protocol integrated with IEEE 802.15.6. ASAP prioritizes patient privacy with randomized node ID generation and temporary shared keys, preventing node tracking and privacy violations. It optimizes network performance by consolidating Master Keys (MK), Pairwise Temporal Keys (PTK), and Group Temporal Keys (GTK) creation into a unified process, ensuring the efficiency of the standard four-message association protocol. ASAP enhances security by eliminating the need for pre-shared keys, reducing the attack surface, and improving forward secrecy. The protocol achieves mutual authentication without pre-shared keys or passwords and supports advanced cryptographic algorithms on nodes with limited processing capabilities. Additionally, it imposes connection initiation restrictions, requiring valid certificates for nodes, thereby addressing gaps in IEEE 802.15.6. Formal verification using Verifpal confirms ASAP's resilience against various attacks. Implementation results show ASAP's superiority over standard IEEE 802.15.6 protocols, establishing it as a robust solution for securing MBAN communications in medical environments.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101363"},"PeriodicalIF":6.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233832","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":"Exploring the boundaries of energy-efficient Wireless Mesh Networks with IEEE 802.11ba","authors":"Roger Sanchez-Vital,&nbsp;Carles Gomez,&nbsp;Eduard Garcia-Villegas","doi":"10.1016/j.iot.2024.101366","DOIUrl":"10.1016/j.iot.2024.101366","url":null,"abstract":"<div><p>In traditional IoT applications, energy saving is essential while high bandwidth is not always required. However, a new wave of IoT applications exhibit stricter requirements in terms of bandwidth and latency. Broadband technologies like Wi-Fi could meet such requirements. Nevertheless, these technologies come with limitations: high energy consumption and limited coverage range. In order to address these two shortcomings, and based on the recent IEEE 802.11ba amendment, we propose a Wi-Fi-based mesh architecture where devices are outfitted with a supplementary Wake-up Radio (WuR) interface. According to our analytical and simulation studies, this design maintains latency figures comparable to conventional single-interface networks while significantly reducing energy consumption (by up to almost two orders of magnitude). Additionally, we verify via real device measurements that battery lifetime can be increased by as much as 500% with our approach.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101366"},"PeriodicalIF":6.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S254266052400307X/pdfft?md5=bb82afe0042ffeccf2459f8320a44178&pid=1-s2.0-S254266052400307X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147574","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":"Combining Multi-Agent Systems and Artificial Intelligence of Things: Technical challenges and gains","authors":"Pedro Hilario Luzolo ,&nbsp;Zeina Elrawashdeh ,&nbsp;Igor Tchappi ,&nbsp;Stéphane Galland ,&nbsp;Fatma Outay","doi":"10.1016/j.iot.2024.101364","DOIUrl":"10.1016/j.iot.2024.101364","url":null,"abstract":"&lt;div&gt;&lt;p&gt;A Multi-Agent System (MAS) usually refers to a network of autonomous agents that interact with each other to achieve a common objective. This system is therefore composed of several software components or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems.&lt;/p&gt;&lt;p&gt;MAS is not a substitute or rival for Artificial Intelligence (AI). Instead, AI techniques can be integrated within agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous.&lt;/p&gt;&lt;p&gt;The Internet of Things (IoT) and AI are two technologies that have been applied for a long time to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Smart behavior, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of AI and IoT, known as AIoT, allows these electronic devices to make more intelligent, autonomous, and automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze these data to derive valuable insights and make informed decisions. The authors devoted their efforts to the critical analysis of AIoT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, &lt;em&gt;the contribution of the authors is in the formulation of innovative research directions, which outline a clear guide for researchers and professionals in the expansion of knowledge in AIoT integration. The results of the research are significant contributions to the continuous advance of the area, enriching the understanding of the challenges and boosting the development of solutions and strategies in this technological convergence&lt;/em&gt;. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results, the research directions are: (&lt;em&gt;i&lt;/em&gt;) Development of a methodology showing how to integrate the different applications independently of the scenarios in which they are deployed. Additionally, elaboration of the tools used in the integration process. (&lt;em&gt;ii&lt;/em&gt;) Deployment of an agent in a microprocessor. (&lt;em&gt;iii","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101364"},"PeriodicalIF":6.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147576","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":"TinyWolf — Efficient on-device TinyML training for IoT using enhanced Grey Wolf Optimization","authors":"Subhrangshu Adhikary ,&nbsp;Subhayu Dutta ,&nbsp;Ashutosh Dhar Dwivedi","doi":"10.1016/j.iot.2024.101365","DOIUrl":"10.1016/j.iot.2024.101365","url":null,"abstract":"<div><p>Training a deep learning model generally requires a huge amount of memory and processing power. Once trained, the learned model can make predictions very fast with very little resource consumption. The learned weights can be fitted into a microcontroller to build affordable embedded intelligence systems which is also known as TinyML. Although few attempts have been made, the limits of the state-of-the-art training of a deep learning model within a microcontroller can be pushed further. Generally deep learning models are trained with gradient optimizers which predict with high accuracy but require a very high amount of resources. On the other hand, nature-inspired meta-heuristic optimizers can be used to build a fast approximation of the model’s optimal solution with low resources. After a rigorous test, we have found that Grey Wolf Optimizer can be modified for enhanced uses of main memory, paging and swap space among <span><math><mrow><mi>α</mi><mo>,</mo><mspace></mspace><mi>β</mi><mo>,</mo><mspace></mspace><mi>δ</mi></mrow></math></span> and <span><math><mi>ω</mi></math></span> wolves. This modification saved up to 71% memory requirements compared to gradient optimizers. We have used this modification to train the TinyML model within a microcontroller of 256KB RAM. The performances of the proposed framework have been meticulously benchmarked on 13 open-sourced datasets.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101365"},"PeriodicalIF":6.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003068/pdfft?md5=ab42e32e095597b7bee6c567498b913a&pid=1-s2.0-S2542660524003068-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147573","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}