Internet Technology Letters最新文献

{"title":"An efficient security and privacy approach for internet of vehicles in vehicular networks for smart cities","authors":"Elham Kariri","doi":"10.1002/itl2.554","DOIUrl":"10.1002/itl2.554","url":null,"abstract":"<p>Intelligent sensing plays a crucial role in making vehicles safe and trouble-free. The purpose of this paper is to introduce Vehicular Sensor Networks (VSNs) in a vehicular IoT-based smart city paradigm, focusing on security. Furthermore, we discuss the robustness and 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 colony optimization for smart cities in which suspicious vehicles are prevented from disseminating messages. IoV real-time communication emphasizes data security. A comparison of experimental results shows that the proposed approach outperforms existing approaches. Smart city communication networks can be optimized using the proposed model.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging 5G and cloud computing for outlier detection in IoT environments: A KNN approach","authors":"S. A. Sahaaya Arul Mary,&nbsp;H. Anwar Basha,&nbsp;G. Mohanraj,&nbsp;R. Kiruthikaa,&nbsp;N. Saranya","doi":"10.1002/itl2.550","DOIUrl":"10.1002/itl2.550","url":null,"abstract":"<p>Internet of Things (IoT) becomes a prominent sensing paradigm between the devices. Its evolution in the global digital increases extensively in various domains. For IoT application's sensors are the primary source for generating data. These collected data are subject to the identification and detection of outliers/anomalies. The massive volume of data generation makes anomaly detection a complex and challenging task. The anomalies affect the data accuracy and data quality. In this paper, the k-NN classifier is proposed for enhancing classification accuracy. K-NN follows a non-parametric strategy and is one of the known classification algorithms. In the proposed system, k-NN is utilized to perform classification or regression with estimations of their k nearest neighbors. The proposed system consists of three major processes such as data preprocessing, classification, visualization. This study explores the utilization of 5G connectivity and cloud computing infrastructure for outlier detection in IoT data streams. Leveraging the K-Nearest Neighbors (KNN) classifier, our methodology focuses on efficiently identifying anomalies in IoT data. By integrating 5G connectivity for real-time data transmission and cloud-based machine learning for scalable analysis, we demonstrate a robust framework for outlier detection in IoT environments. The Experimental work with the proposed method is carried out using training and observation is tabulated with respective classes. As a result, on the three metrics, the proposed k-NN proves its efficiency is far better than the others, with an average of 98.4% of accuracy.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning","authors":"L. Srinivasan,&nbsp;Humaira Nishat,&nbsp;S. Shargunam,&nbsp;Deepak Kumar Nayak,&nbsp;K. Janani","doi":"10.1002/itl2.556","DOIUrl":"10.1002/itl2.556","url":null,"abstract":"<p>In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask-RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting-edge technology to address optimization challenges in modern video streaming systems, focusing on the real-time optimization of video streams in contemporary technological environments.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient clustering algorithm using distributed fuzzy-logic to prolong the survivability of wireless sensor networks","authors":"Lulwah M. Alkwai,&nbsp;Kusum Yadav","doi":"10.1002/itl2.549","DOIUrl":"10.1002/itl2.549","url":null,"abstract":"<p>Energy efficiency is critical for prolonging the survivability of wireless sensor networks (WSNs), and clustering algorithms play a significant role in achieving this goal. An application-specific wireless sensor network requires adapted methods and techniques to meet its requirements. A vast amount of research has been done on optimizing energy consumption and enhancing network lifetime of sensor nodes. To increase the lifetime of WSNs, we present and evaluate an energy-efficient clustering algorithm based on distributed fuzzy logic (EECADFL). High reliability, low error rates during clustering, and its ability to perform well in large-scale networks with many nodes are some of the main benefits of this method. In wireless sensor networks, simulation results showed that the scheme provided better lifetime performance while limiting dead nodes and improving cluster head selection.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Everything you wanted to know about ChatGPT: Components, capabilities, applications, and opportunities","authors":"Arash Heidari,&nbsp;Nima Jafari Navimipour,&nbsp;Sherali Zeadally,&nbsp;Vinay Chamola","doi":"10.1002/itl2.530","DOIUrl":"https://doi.org/10.1002/itl2.530","url":null,"abstract":"<p>Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum key distribution (QKD) for wireless networks with software-defined networking","authors":"Hayder Sabeeh Hadi,&nbsp;Ahmed J. Obaid","doi":"10.1002/itl2.547","DOIUrl":"https://doi.org/10.1002/itl2.547","url":null,"abstract":"<p>Network management has been significantly transformed by software-defined networking (SDN), which consolidates control and improves adaptability. As a result of this paradigm shift, security concerns have emerged concurrently. Within SDN environments, maintaining service path integrity is of utmost importance because malicious entities can exploit network flows to gain access to data, resulting in security breaches. Thus, QKD networks require a management plane that can control and manage the QKD resources. Quantum key distribution networks can be controlled and managed separately by SDN, allowing quantum key forwarding to be separated from their control and management. The paper provides an overview of QKD networks enabled by SDN, along with an overview of its architecture, interfaces, and protocols. This paper elaborates on the overall architecture and related interfaces and protocols of QKD networks assisted by SDN. Then, three important paradigms for simulation are presented using three use cases.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation and investigation of diffraction optical elements effect on bit error rate of free space optics and performance investigation of space uplink wireless optical communication under varying atmospheric turbulence conditions","authors":"Gaurav Soni,&nbsp;Manish Sharma","doi":"10.1002/itl2.538","DOIUrl":"10.1002/itl2.538","url":null,"abstract":"<p>Several other optical antenna topologies have been developed and implemented throughout the years. These topologies include a variety of optical components, including the axicon optical element, dual-secondary mirror, cone reflecting mirror, prism beam slier, and beam-splitter/beam combiner. In contrast, the secondary reflecting mirror causes an obscuration loss that must be compensated for by reducing the transmission power in an optical antenna design. In order to address this issue in space optical communication, the present research helps to develop an enhanced two diffractive optical elements (DOEs) technology however the data presented therein only shows that DOEs may boost transmission power efficiency, which is insufficient for system designers. Though On-Off Keying (OOK) is widely used in optical communication systems at the moment, the proposed research include DOEs into an OOK space uplink optical. The proposed research uses numerical simulation to explore how much a space uplink OOK system's bit error rate (BER) may be improved by using DOEs and adjusting fundamental parameters. The proposed BER model takes environmental factors like wind and detector noise into account. Using this theoretical model, the present work helps to investigate the effect of DOEs on the BER versus fundamental parameter characteristic curves in space uplink optical communication. Based on the findings, the DOEs structure has the potential to significantly enhance the BER performance of space uplink optical communication systems, especially at high obscuration ratios. When the obscuration ratio is 0.25, 0.167, or 0.125 and the transmission power is 1 W, for instance, the DOEs may improve the BER by a factor of two or one order of magnitude or less when the parameters are changed to the typical parameter values as specified. Results increase by a factor of six, three, and two orders of magnitude, respectively, when transmitting at 5 W. The results show that DOEs can significantly enhance the BER performance, especially at high obscuration ratios. The findings suggest that integrating DOEs into the optical subsystem is a straightforward approach to improving the performance of space uplink optical communication systems.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications","authors":"R. Krishna Priya,&nbsp;Nitin N. Sakhare,&nbsp;Ajay Paithane,&nbsp;R. Shekhar,&nbsp;M. Sabarimuthu","doi":"10.1002/itl2.539","DOIUrl":"10.1002/itl2.539","url":null,"abstract":"<p>A Stochastic Low-Density Parity-Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal-to-noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC-ASQ-KLMSA), this paper proposes an area-efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE-T standard and used in this study. We examine the ASQ-KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter-connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ-KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub-node of the degree-6 VN was changed with an adder-based 5–1 input grouping sub-node for the (2048, 1723) code in order to get more accurate results when the check-to-variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC-ASQ-KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher-performance stochastic LDPC decoder architecture designed through correlation analysis (HP-SLDPC-CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit-Serial Stochastic Updating(HLDPC-BSSU), Flexible FPGA-Based Stochastic Decoder for 5G LDPC codes (FPGA-SD-5G-LDPC), respectively.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced channel prediction in large-scale 5G MIMO-OFDM systems using pyramidal dilation attention convolutional neural network","authors":"Chirakkal Radhakrishnan Rathish,&nbsp;Balakrishnan Manojkumar,&nbsp;Lakshmanaperumal Thanga Mariappan,&nbsp;Panchapakesan Ashok,&nbsp;Udayakumar Arun Kumar,&nbsp;Krishnan Balan","doi":"10.1002/itl2.532","DOIUrl":"10.1002/itl2.532","url":null,"abstract":"<p>In order to enhance communication while minimizing complexity in 5G and beyond, MIMO-OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, 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-noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP-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.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety protection using artificial intelligence internet of things for preschool education","authors":"Yun Tan,&nbsp;Shuangyuan Mo","doi":"10.1002/itl2.537","DOIUrl":"10.1002/itl2.537","url":null,"abstract":"<p>With the rapid development of social economy and information technology, safety protection in daily life has become more and more important. Although the awareness of safety has increased, the children's safety is still not paid enough attention. Children still may suffer accidental injuries, especially in developing countries. Children spend most of time at school in a day. Thus, it has become an emergent challenge to guarantee children's safety at school. In order handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) safety protection system for preschool education. The AIoT safety protection system consists of three parts: camera, Raspberry Pi, and monitoring computer. The camera captures the images of classroom scene during preschool education. The Raspberry Pi analyzes the images from camera to determine the unsafe behaviors of children, in which a YOLOv8 model is deployed. The monitoring computer receives the alarms from Raspberry Pi. The camera, Raspberry Pi, and monitoring computer are connected using wireless sensor network. The experiments show the behavior recognition model can correctly identify most of dangerous behaviors of children in classroom. The simulation result demonstrates the AIoT safety protection system can find the dangerous behaviors in time.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}