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BDD efficiency: Survey of BDD edge ordering algorithms in network reliability BDD 效率：网络可靠性中的 BDD 边排序算法调查
IF 0.9
Internet Technology Letters Pub Date : 2024-04-01 DOI: 10.1002/itl2.525
Aakash Chauhan, Gourav Verma
{"title":"BDD efficiency: Survey of BDD edge ordering algorithms in network reliability","authors":"Aakash Chauhan,&nbsp;Gourav Verma","doi":"10.1002/itl2.525","DOIUrl":"10.1002/itl2.525","url":null,"abstract":"<p>Network reliability analysis is vital for ensuring efficient and error-free communication within networking and communication applications. Binary Decision Diagrams (BDDs) have emerged as a powerful tool for analyzing and optimizing complex network infrastructures. The objective of this research paper is to conduct a comparative analysis of edge-ordering algorithms for network reliability using BDDs the study aims to evaluate and compare existing algorithms, providing valuable insights for selecting suitable edge-ordering algorithms that enhance network reliability. The paper concludes that snooker is outperforming among selected algorithms.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140756351","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}
引用次数: 0
A semantic big data analysis method based on enhanced neural networks in IoT 基于增强型神经网络的物联网语义大数据分析方法
IF 0.9
Internet Technology Letters Pub Date : 2024-03-28 DOI: 10.1002/itl2.524
Chongke Wang
{"title":"A semantic big data analysis method based on enhanced neural networks in IoT","authors":"Chongke Wang","doi":"10.1002/itl2.524","DOIUrl":"10.1002/itl2.524","url":null,"abstract":"<p>Due to the growth of neural networks, the semantic big data analysis method can classify images at the pixel level, which is very suitable for the needs of IoT. In semantic big data analysis methods, the DeepLab algorithm is an improved and highly accurate algorithm based on enhanced neural networks. However, the DeepLab algorithm does not fully utilize global information, resulting in poor performance for complex scenes. Therefore, this article makes improvements by introducing a global context information module and providing prior information of complex scenes in images. It extracts global information and merges with original features. It improves the expression ability of features. This global context can enhance the accuracy of semantic big data analysis method, and an attention mechanism is designed. The experimental results display that the improved DeepLab semantic big data analysis method based on self-attention and global context module has good average pixel accuracy and average intersection to union ratio performance on the Pascal VOC 2012 dataset. And the improvement effect is significant.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371956","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}
引用次数: 0
Multi-objective prairie dog optimization algorithm for IoT-based intrusion detection 基于物联网的入侵检测多目标草原犬优化算法
IF 0.9
Internet Technology Letters Pub Date : 2024-03-21 DOI: 10.1002/itl2.516
Shubhkirti Sharma, Vijay Kumar, Kamlesh Dutta
{"title":"Multi-objective prairie dog optimization algorithm for IoT-based intrusion detection","authors":"Shubhkirti Sharma,&nbsp;Vijay Kumar,&nbsp;Kamlesh Dutta","doi":"10.1002/itl2.516","DOIUrl":"10.1002/itl2.516","url":null,"abstract":"<p>Detecting unauthorized access, unusual activities, and data is significant for the security of IoT networks as it helps identify malfunctioning, faults, and intrusions. Intrusion detection methods analyze network information to identify potential misuse or intrusion attacks. This research proposes a multi-objective prairie dog optimization algorithm (MPDA) to improve its ability to deal with feature selection problems. The proposed algorithm is modified by incorporating the concepts of an archive, grid, and non-dominance. An archive and a grid are used to save intermediate best results and improve the diversity, respectively. The non-dominance concept is employed to deal with multiple objectives. On the NSL-KDD, CIC-IDS2017, and IoTID20 datasets, MPDA achieves fewer features, higher accuracy, and lower false alarm rates. MPDA outperforms simple classifiers and state-of-art multiobjective optimization algorithms in intrusion detection.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221334","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}
引用次数: 0
Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy 面向工业场景效率优化的语义传感器数据标注方法，助力数字经济发展
IF 0.9
Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.508
Na Tao, Tao Zhang
{"title":"Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy","authors":"Na Tao,&nbsp;Tao Zhang","doi":"10.1002/itl2.508","DOIUrl":"https://doi.org/10.1002/itl2.508","url":null,"abstract":"<p>In the digital economy era, efficiently leveraging the vast amount of sensor data generated by the Industrial Internet of Things (IIoT) is essential. This paper presents an innovative semantic annotation method for industrial sensor data, designed to optimize data processing and enhance system efficiency. Our method combines cluster analysis, ontology development, and rule-based reasoning to automatically annotate IIoT sensory data. By utilizing data aggregation and filtering mechanisms, which incorporate the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and a rule engine, we significantly reduce the data volume required for annotation. The Semantic Web Rule Language aids in naming new concepts and properties identified through clustering, contributing further to the automation of data processing. Experimental results, using public datasets, validate the effectiveness of our method, showing a reduction in data volume by about 20% and underscoring its potential in enhancing industrial systems' automation and overall efficiency.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597150","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}
引用次数: 0
Wearing sensor data integration for promoting the performance skills of music in IoT 整合穿戴式传感器数据，提升物联网音乐表演技能
IF 0.9
Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.517
Xiaochan Li, Yi Shi, Daohua Pan
{"title":"Wearing sensor data integration for promoting the performance skills of music in IoT","authors":"Xiaochan Li,&nbsp;Yi Shi,&nbsp;Daohua Pan","doi":"10.1002/itl2.517","DOIUrl":"https://doi.org/10.1002/itl2.517","url":null,"abstract":"<p>This study integrates multi-node wearable sensor data to improve music performance skills. A window-adding method is used during time-frequency feature extraction. By incorporating kernel functions, we present a generalized discriminant analysis (GDA) method to reduce the high-dimensional sensor features while retaining performance traits. Experiments demonstrate that the proposed GDA approach achieves higher accuracy (92.71%), precision (90.54%), and recall (88.68%) compared to linear discriminant analysis (82.39% accuracy) and principal component analysis (88.56% accuracy) in classifying motions performed by music performers. The integrated analysis of wearable sensor data facilitates comprehensive feedback to strengthen proficiency across various music performance skills.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597149","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}
引用次数: 0
Multimodal information fusion method in emotion recognition in the background of artificial intelligence 人工智能背景下情绪识别中的多模态信息融合方法
IF 0.9
Internet Technology Letters Pub Date : 2024-03-12 DOI: 10.1002/itl2.520
Zhen Dai, Hongxiao Fei, Chunyan Lian
{"title":"Multimodal information fusion method in emotion recognition in the background of artificial intelligence","authors":"Zhen Dai,&nbsp;Hongxiao Fei,&nbsp;Chunyan Lian","doi":"10.1002/itl2.520","DOIUrl":"10.1002/itl2.520","url":null,"abstract":"<p>Recent advances in Semantic IoT data integration have highlighted the importance of multimodal fusion in emotion recognition systems. Human emotions, formed through innate learning and communication, are often revealed through speech and facial expressions. In response, this study proposes a hidden Markov model-based multimodal fusion emotion detection system, combining speech recognition with facial expressions to enhance emotion recognition rates. The integration of such emotion recognition systems with Semantic IoT data can offer unprecedented insights into human behavior and sentiment analysis, contributing to the advancement of data integration techniques in the context of the Internet of Things. Experimental findings indicate that in single-modal emotion detection, speech recognition achieves a 76% accuracy rate, while facial expression recognition achieves 78%. However, when state information fusion is applied, the recognition rate increases to 95%, surpassing the national average by 19% and 17% for speech and facial expressions, respectively. This demonstrates the effectiveness of multimodal fusion in emotion recognition, leading to higher recognition rates and reduced workload compared to single-modal approaches.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250633","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}
引用次数: 0
PAPR reduction using model-driven hybrid algorithms in the 6G NOMA waveform 在 6G NOMA 波形中使用模型驱动混合算法降低 PAPR
IF 0.9
Internet Technology Letters Pub Date : 2024-03-05 DOI: 10.1002/itl2.515
Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong
{"title":"PAPR reduction using model-driven hybrid algorithms in the 6G NOMA waveform","authors":"Arun Kumar,&nbsp;Nishant Gaur,&nbsp;Aziz Nanthaamornphong","doi":"10.1002/itl2.515","DOIUrl":"10.1002/itl2.515","url":null,"abstract":"<p>In the evolving landscape of sixth-generation (6G) network technologies, Non-Orthogonal Multiple Access (NOMA) systems are pivotal for achieving enhanced spectral efficiency and network capacity. However, a significant challenge in NOMA systems is the high Peak-to-Average Power Ratio (PAPR), which undermines system efficiency by necessitating high-power amplifiers (HPAs) to operate in their less efficient, non-linear range. Addressing this, we introduce a novel hybrid approach, the Selective Mapping-Circular Transformation Method (SLM-CTM), which ingeniously amalgamates the strengths of Selective Mapping (SLM) and the Circular Transformation Method (CTM) to mitigate PAPR issues. SLM is renowned for its peak power reduction capabilities without adding to system complexity, whereas CTM is valued for its simplicity and controlled signal distortion. The proposed SLM-CTM strategy employs a blend of linear and nonlinear techniques to effectively lower PAPR in non-orthogonal NOMA configurations, thereby reducing high-power peaks while simultaneously enhancing signal quality. This paper delineates the application of the SLM-CTM algorithm to evaluate critical NOMA parameters such as Power Spectral Density (PSD), Bit Error Rate (BER), and PAPR. Simulation results highlight the efficacy of SLM-CTM over conventional SLM, demonstrating a significant throughput improvement of 3.2 dB and a PAPR reduction of 4.6 dB, underscoring the potential of SLM-CTM in elevating the performance of NOMA systems within 6G network.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263278","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}
引用次数: 0
On the physical layer security performance of full-duplex cooperative NOMA system with multiple eavesdroppers, imperfect SIC and hardware imperfections 关于具有多个窃听器、不完善 SIC 和硬件缺陷的全双工合作 NOMA 系统的物理层安全性能
IF 0.9
Internet Technology Letters Pub Date : 2024-03-04 DOI: 10.1002/itl2.513
T. Nimi, A. V. Babu
{"title":"On the physical layer security performance of full-duplex cooperative NOMA system with multiple eavesdroppers, imperfect SIC and hardware imperfections","authors":"T. Nimi,&nbsp;A. V. Babu","doi":"10.1002/itl2.513","DOIUrl":"10.1002/itl2.513","url":null,"abstract":"<p>In this letter, we propose a control jammer-assisted framework for improving the physical layer security (PLS) of full-duplex (FD) - cooperative non-orthogonal multiple access (FD-CNOMA) network. We derive analytical expressions for the secrecy outage probabilities (SOPs) of the users for the jammer-assisted and the no-jammer scenarios, considering multiple non-colluding eavesdroppers, residual hardware impairments and imperfect successive interference cancellation conditions. It is demonstrated that the proposed jammer-assisted framework provides significant reduction of the SOPs experienced by the downlink users in FD-CNOMA network.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266736","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}
引用次数: 0
The impact of information disclosure on investment returns in the era of internet of things technology
IF 0.9
Internet Technology Letters Pub Date : 2024-02-28 DOI: 10.1002/itl2.514
Hui Peng, Zhixing Hu
{"title":"The impact of information disclosure on investment returns in the era of internet of things technology","authors":"Hui Peng,&nbsp;Zhixing Hu","doi":"10.1002/itl2.514","DOIUrl":"https://doi.org/10.1002/itl2.514","url":null,"abstract":"<p>The development of the Internet of Things has brought a large amount of data, and how to efficiently process this data has become an important issue. The timeliness of information disclosure can make the Internet of Things data processing process more efficient and extract valuable information, providing support for decision-making and optimization. This paper is based on the need for timely information disclosure in the era of Internet of Things technology, constructs the measure of Timeliness of information disclosure and uses such a measure in the five-factor model to test its validity. The results show that Timeliness of information disclosure can optimize the five-factor model and significantly improve returns on investment.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363083","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}
引用次数: 0
Towards data privacy in a fog of things 在物联网迷雾中保护数据隐私
IF 0.9
Internet Technology Letters Pub Date : 2024-02-24 DOI: 10.1002/itl2.512
George Pacheco Pinto, Cassio Prazeres
{"title":"Towards data privacy in a fog of things","authors":"George Pacheco Pinto,&nbsp;Cassio Prazeres","doi":"10.1002/itl2.512","DOIUrl":"10.1002/itl2.512","url":null,"abstract":"<p>Data from the Internet of Things (IoT) devices enable the design of new business models and services, improving user experience and satisfaction. However, these devices also collect personal data and place them on centralized servers without transparency and user control, enhancing data privacy concerns (such as identification, localization and tracking, profiling, and linkage). We propose the Fog of Things (FoT)-Personal Data Stores (PDS) paradigm, an extension of the (FoT) paradigm tailored to protect personal data privacy in the IoT, specifically in the FoT. We propose a data decentralization solution through Personal Data Stores, empowering users with control over their data. We present an architectural framework based on this concept and an implementation of a usage scenario.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140433884","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}
引用次数: 0
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