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Internet Technology Letters
John Wiley & Sons Ltd.
Internet Technology Letters

John Wiley & Sons Ltd.

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    An efficient security and privacy approach for internet of vehicles in vehicular networks for smart cities

    Elham Kariri
    e554.1-e554.6页
    查看更多>>摘要：Intelligent sensing plays a crucial role in making vehicles safe and trouble-free. Thepurpose of this paper is to introduce Vehicular Sensor Networks (VSNs) in a vehicularIoT-based smart city paradigm, focusing on security. Furthermore, we discuss the robustnessand reliability of VSN. In this design, Ad hoc On-Demand Distance Vector (AODV)routing-based Internet of Vehicles is integrated with a privacy-aware secure ant colonyoptimization for smart cities in which suspicious vehicles are prevented from disseminatingmessages. IoV real-time communication emphasizes data security. A comparison ofexperimental results shows that the proposed approach outperforms existing approaches.Smart city communication networks can be optimized using the proposed model.
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    • NETL
    • NSTL
    • Wiley

    Leveraging Cloud and IoT-Based NLP in Virtual Reality for Sustainable Industry Innovation: A Pathway to Achieving SDG 9 (Industry, Innovation, and Infrastructure)

    Gyana Prakash BhuyanBibhu Kalyan MishraGopinath Palai
    e659-1-e659.6页
    查看更多>>摘要：This paper explores the integration of cloud computing, IoT, Natural Language Processing (NLP), and virtual reality (VR) as apathway to achieving Sustainable Development Goal (SDG) 9, which focuses on fostering industry innovation, building resilientinfrastructure, and promoting sustainable industrialization. The convergence of these technologies provides a powerful toolkit fordriving the next wave of industrial digital transformation. Cloud platforms such as AWS, Azure, IBM Watson, and Google Cloudoffer scalability, flexibility, and efficiency, enabling industries to adopt advanced technologies like NLP and VR without significantupfront capital investment. By leveraging IoT for real-time monitoring and data collection, industries can optimize processes,reduce energy consumption, and enhance predictive maintenance, contributing to sustainability. NLP enables natural and efficienthuman-machine interaction, automating key processes and improving decision-making. The use of VR allows for immersivesimulations that enhance worker training, product design, and infrastructure management, reducing waste and increasing safety.This combination of technologies fosters innovation, inclusivity, and sustainability in industrial operations. The paper highlightspractical applications of these technologies, including smart manufacturing, predictive maintenance, and infrastructure monitoring,while emphasizing the role of cloud platforms in democratizing access to advanced tools for industries worldwide. Ultimately,this approach supports the achievement of SDG 9 by enabling more sustainable, resilient, and inclusive industrial practices.
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    • NETL
    • NSTL
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    EEERP-RL: Enhanced energy-efficient routing protocol based on reinforcement learning for wireless sensor network

    Mohanad J. JaberZahraa Jasim Jaber
    e548.1-e548.6页
    查看更多>>摘要：Wireless Sensor Networks (WSN) efficiently monitors and record environmentalconditions, transmitting this data to central locations via widely distributed,sensor nodes. Onemajor challenge inWSN involves creating an energy-efficientrouting protocol that minimizes energy consumption and extends the network’slongevity. In this paper, we propose EEERP-RL, an enhanced energy-efficientQoS routing protocol for WSNs, based on reinforcement learning (RL). The proposedprotocol has been compared with two other protocols to determine whichone gives the best performance in the network OSPF and SDN-Q. There is aninvestigation of packet delivery ratios and delays (m), as well as the impact ofalive nodes, dead nodes, and energy consumption. Based on simulation results,the proposed protocol outperforms as compared to existing protocol in terms ofdifferent network traffic loads and node mobility.
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    • NETL
    • NSTL
    • Wiley

    Lightweight facial expression estimation for mobile computing in portable device

    Jinming Liu
    e533.1-e533.6页
    查看更多>>摘要：Facial expression recognition has been studied for many years, especially withthe development of deep learning. However, the existing researches still havethe following two issues. Firstly, the intensity of facial expression is neglected.Secondly, the deep learning based approaches cannot be directly deployed in thedevices with limited resources. In order to tackle these two issues, this paper proposesa lightweight facial expression estimation method using a shallow ordinalregression algorithm, which is deployed in a portable smart device for mobilecomputing in IoTs. Compared with classification based facial expression recognitionmethods, ordinal regression considers the intensity of facial expression toachieve bettermean absolute error (MAE), which is validated by experiments onseveral public facial expression datasets. The simulation in portable device alsodemonstrates its effectiveness for mobile computing.
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    • NETL
    • NSTL
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    Plant disease detection using machine learning techniques based on internet of things (IoT) sensor network

    Bere Sachin SukhadeoYogita Deepak SinkarSarika Dilip DhurgudeShashikant V. Athawale...
    e546.1-e546.6页
    查看更多>>摘要：In recent years, smart agriculture has grown rapidly. A crop disease is generallycaused by pests, insects, or pathogens and reduces the productivity of the crop byadversely affecting its yield. There is a severe loss of crops across the country dueto various crop diseases, and one reason is not being able to detect the disease inits early stages keeps them from finding a solution. An Internet of Things (IOT)sensor network is used to detect and classify diseases in leaves in this paper.Precision agriculture usesmachine learning techniques to increase crop growth,control the cultivation process, and enhance crop productivity with less humaninvolvement. IOT sensor networks are being used in precision agriculture usingmachine learning techniques. A result of the proposedmethod shows an overallaccuracy of 88%.
      原文链接:
    • NETL
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    Enhanced channel prediction in large-scale 5G MIMO-OFDM systems using pyramidal dilation attention convolutional neural network

    Chirakkal Radhakrishnan RathishBalakrishnan ManojkumarLakshmanaperumal Thanga MariappanPanchapakesan Ashok...
    e532.1-e532.6页
    查看更多>>摘要：In order to enhance communication while minimizing complexity in 5Gand beyond,MIMO-OFDMsystems need accurate channel prediction. In order to enhance channel prediction, decrease ErrorVector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs thePyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping withfiltering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with thereduced data. By combining attention techniques with pyramidal dilated convolutions, the suggestedPDACNN architecture is able to extract OFDM channel parameters across several scales. Attentionapproaches enhance channel prediction by allowing the model to dynamically concentrate onessential information. The primary objective is to make use of the network’s ability to comprehendintricate spatial–temporal connections in OFDM channel data. The goal of these techniques is tomake channel forecasts more accurate and resilient while decreasing concerns about EVM, PeakPower, and ACLR. To confirm the effectiveness of the suggested CP-LSMIMO-OFDM-PDACNN,we measure its spectral efficiency, peak-to-average power ratio, bit error rate (BER), signal-to-noiseratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved viaCP-LSMIMO-OFDM-PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced.PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.
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    • NETL
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    Semantic sensor data integration for talent development via hybrid multi-objective evolutionary algorithm

    Fang LuoYa-Juan YangYu-Cheng Geng
    e557.1-e557.6页
    查看更多>>摘要：In this work, we propose a new hybridMulti-Objective Evolutionary Algorithm(hMOEA) specifically designed for semantic sensor data integration, targetingtalent development within the burgeoning field of the Semantic Internetof Things (SIoT). Our approach synergizes the capabilities of Multi-ObjectiveParticle Swarm Optimization and Genetic Algorithms to tackle the sophisticatedchallenges inherent in Sensor Ontology Matching (SOM). This innovativehMOEA framework is adapt at discerning precise semantic correlations amongdiverse ontologies, thereby facilitating seamless interoperability and enhancingthe functionality of IoT applications. Central to our contributions are the developmentof an advanced multi-objective optimization model that underpins theSOM process, the implementation of the hMOEA framework which sets a newbenchmark for accurate semantic sensor data integration, and the rigorous validationof hMOEA’s superiority through extensive testing in varied real-worldSOM scenarios. This research not only marks a significant advancement inSOM but also highlights the critical role of cutting-edge SOM methodologiesin educational curricula, for example, the new business subject education proposedby China in recent years, aimed at equipping future professionalswith thenecessary skills to innovate and lead in the SIoT and SWdomains.
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    • NETL
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    • Wiley

    The Traffic Safety Assessment Model for Mixed Urban Traffic Based on Driving Safety Field and ICVs

    Renjie WangJing Cheng
    e653.1-e653.8页
    查看更多>>摘要：To accurately assess the driving risks associated with mixed traffic scenarios in urban areas and align with the direction of Internetof Things (IoT) technologies. An intelligent connectivity traffic safety assessment model for mixed urban traffic based on the ICVsis proposed. First, this paper proposes the traffic safety assessment model based on the driving safety field, the model integratespotential, kinetic, and behavior fields. In the process of establishing the mode, we have incorporated the acceleration parameterto dynamically capture driving risk trends. Subsequently, we define a mixed traffic scenario and calculate the driving risks forroad users under different driving states based on this algorithm. The results demonstrate that the model effectively captures thedriving risks of road users in different states, and the evaluation outcomes align with real-world situations, thereby validatingits effectiveness. The significance of this research lies in providing a theoretical foundation for the application of the Internet ofThings (IoT) in complex traffic scenarios and supporting future route planning and driving safety decision-making in intelligenttransportation systems. Additionally, this model presents new ideas and methods for the development and application of ICVstechnology, contributing to the advancement of intelligent transportation systems.
      原文链接:
    • NETL
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    Research on the application of English short essay reading emotional analysis in online English teaching under IoT scenario

    Xiaoli Zhan
    e535.1-e535.6页
    查看更多>>摘要：Speech-emotion analysis plays an important role in English teaching. The existingconvolutional neural networks (CNNs) can fully explore the spatial featuresof speech information, and cannot effectively utilize the temporal dependenceof speech signals. In addition, it is difficult to build a more efficient and robustsentiment analysis system by solely utilizing speech information. With thedevelopment of the Internet of Things (IoTs), online multimodal information,including speech, video, and text, has become more convenient. To this end, thispaper proposes a novel multimodal fusion emotion analysis system. Firstly, bycombining convolutional networks with Transformer encoders, the spatiotemporaldependencies of speech information are effectively utilized. To improvemultimodal information fusion, we introduce the exchange-based fusionmechanism.The experimental results on the public dataset indicate that the proposedmultimodal fusion model achieves the best performance. In online Englishteaching, teachers can effectively improve the quality of teaching by leveragingthe feedback information of students’ emotional states through our proposeddeep model.
      原文链接:
    • NETL
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    Safety protection using artificial intelligence internet of things for preschool education

    Yun TanShuangyuan Mo
    e537.1-e537.6页
    查看更多>>摘要：With the rapid development of social economy and information technology,safety protection in daily life has become more and more important. Althoughthe awareness of safety has increased, the children’s safety is still not paidenough attention. Children still may suffer accidental injuries, especially indeveloping countries. Children spend most of time at school in a day. Thus, ithas become an emergent challenge to guarantee children’s safety at school. Inorder handle this issue, this paper designs an Artificial Intelligence Internet ofThings (AIoT) safety protection systemfor preschool education. TheAIoT safetyprotection system consists of three parts: camera, Raspberry Pi, and monitoringcomputer. The camera captures the images of classroom scene during preschooleducation. The Raspberry Pi analyzes the images from camera to determine theunsafe behaviors of children, in which a YOLOv8 model is deployed. The monitoringcomputer receives the alarms from Raspberry Pi. The camera, RaspberryPi, and monitoring computer are connected using wireless sensor network. Theexperiments show the behavior recognition model can correctly identify mostof dangerous behaviors of children in classroom. The simulation result demonstratesthe AIoT safety protection system can find the dangerous behaviors intime.
      原文链接:
    • NETL
    • NSTL
    • Wiley