Internet Technology Letters最新文献

Association Problem Between Multiple Jammers and Multiple Wardens in Covert Communication Networks With TCIPC 基于TCIPC的隐蔽通信网络中多干扰者与多守卫者的关联问题

IF 0.9

Internet Technology Letters Pub Date : 2025-05-31 DOI: 10.1002/itl2.70049

Fanfeng Shi, Xinfeng Zhu

{"title":"Association Problem Between Multiple Jammers and Multiple Wardens in Covert Communication Networks With TCIPC","authors":"Fanfeng Shi, Xinfeng Zhu","doi":"10.1002/itl2.70049","DOIUrl":"https://doi.org/10.1002/itl2.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>Network survivability refers to the capability of the networks to maintain a guaranteed quality of service (QoS) in the presence of malicious attacks and node failures. The covert wireless communication (CWC) technique can improve the survivability of wireless Internet of Things (IoT) networks by hiding the desired signals in background noises. In this paper, the CWC scheme for the multiple jammers and multiple wardens networks is investigated, where the truncated channel inversion power control (TCIPC) is applied. The outage probability is utilized to evaluate the communication effectiveness, and the detection error probability (DEP) is used to evaluate the covertness. Then, the association problem between the multiple jammers and the multiple wardens is studied. The optimal association problem is formulated as a min-max integer programming problem to minimize the outage probability between Alice and Bob, subject to the DEP constraints, which are hard to solve. It is demonstrated that the genetic algorithm is a valid method to solve the proposed min-max integer programming problem, and the performances are analyzed.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179258","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

Nadam-Swarm Based Adaptive Routing Protocol Using Graph Equivariant Network for Seamless Data Transmission in 5G-Connected Wireless Sensor Networks 基于Nadam-Swarm的图等变网络自适应路由协议在5g连接无线传感器网络中的无缝数据传输

IF 0.9

Internet Technology Letters Pub Date : 2025-05-29 DOI: 10.1002/itl2.70048

Smita Bhore, Narambunathan Arunachalam Natraj, V. Suresh, M. S. Mohamed Mallick, Sunil Lavadiya

{"title":"Nadam-Swarm Based Adaptive Routing Protocol Using Graph Equivariant Network for Seamless Data Transmission in 5G-Connected Wireless Sensor Networks","authors":"Smita Bhore, Narambunathan Arunachalam Natraj, V. Suresh, M. S. Mohamed Mallick, Sunil Lavadiya","doi":"10.1002/itl2.70048","DOIUrl":"https://doi.org/10.1002/itl2.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless Sensor Networks (WSNs) have transformed data transmission methodologies by merging with 5G technology to provide ultra-reliable, low-latency, and energy-efficient data transfers. Nonetheless, owing to the intricacies involved in attaining dynamic network topologies, constrained resource management, and scalability, there is a want for improved routing methodologies to optimize 5G-enabled wireless sensor networks. This study introduces the “Nadam-Swarm based Adaptive Routing Protocol using Graph Equivariant Network for Seamless Data Transmission in 5G-Connected Wireless Sensor Networks” (NR-GE-BiSO) as a proficient solution for efficient data transmission. The protocol utilizes a multi-tiered approach: the Nadam-based Random Search Algorithm (NR-SA) dynamically allocates clustering head nodes to balance the load depending on the residual energy and traffic density of the nodes inside the network. Graph Equivariant Quantum Neural Networks (GE-QNN) provide a Wireless Sensor Network (WSN) structural graph to identify optimal routing pathways based on variations within the WSN, facilitating effective data delivery with minimal power consumption. The Bipolar Swarm Optimizer (BiSO) enhanced the routing process by determining the shortest, most energy-efficient routes with minimal latency and energy expenditure. Simulation results validate the efficacy of NR-GE-BiSO, achieving metrics: 99.92% throughput and a 99.88% packet delivery ratio with 99.01% reduction of routing overhead outperforming the existing methods. The findings indicated that the protocol facilitates energy-efficient, scalable, and reliable communication. By integrating 5G capabilities with advanced routing algorithms, NR-GE-BiSO achieves a heightened degree of wireless sensor network efficiency, enabling innovative applications in smart cities, industrial IoT, and environmental domains.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171208","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 Tiny Machine Learning for Real-Time Anomaly Detection of Self-Media Public Opinion in Edge-Cloud-Cooperation Campus Networks 基于微型机器学习的边缘云合作校园网自媒体舆情实时异常检测

IF 0.9

Internet Technology Letters Pub Date : 2025-05-28 DOI: 10.1002/itl2.70038

Shiqi Li

{"title":"A Tiny Machine Learning for Real-Time Anomaly Detection of Self-Media Public Opinion in Edge-Cloud-Cooperation Campus Networks","authors":"Shiqi Li","doi":"10.1002/itl2.70038","DOIUrl":"https://doi.org/10.1002/itl2.70038","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-time anomaly detection in self-media public opinion requires lightweight solutions to address the latency and multimodal complexity challenges of campus network ecosystems. This article proposes a tiny machine learning framework for edge-cloud-cooperation campus networks, enabling efficient detection of opinion anomalies through distributed computation. The architecture combines edge-native micro-model compression with cloud-assisted federated verification, achieving three key innovations: (1) On-device micro-graph neural networks (GNNs) deployed at edge nodes for low-latency pattern recognition in terahertz multimedia streams; (2) a dual-phase anomaly engine leveraging contrastive semantic alignment and adaptive influence analysis to capture cross-modal inconsistencies; (3) dynamic knowledge distillation that reduces model footprints to 8 MB while preserving 91% precision and 87% recall on a 120,000 post dataset from 15 universities. Experimental results demonstrate 120 ms average inference latency with 68% lower computation overhead than centralized baselines, accelerating emergency response by 3.25× through edge-cloud task partitioning. The framework maintains 74% energy efficiency in continuous operation, proving the viability of tiny machine learning paradigms for intelligent campus governance without relying on next-generation communication standards.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148563","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 Multiscale Transformer Framework for Optimizing Educational Resource Transmission in Preschool Wireless Networks 一种优化学前无线网络教育资源传输的多尺度变压器框架

IF 0.9

Internet Technology Letters Pub Date : 2025-05-23 DOI: 10.1002/itl2.70043

Junqing Fan

引用次数: 0

A Secure and Trusted Communication Solution for Web 3.0 Based on Edge Intelligence 基于边缘智能的Web 3.0安全可信通信解决方案

IF 0.9

Internet Technology Letters Pub Date : 2025-05-23 DOI: 10.1002/itl2.70007

Geetanjali Rathee, Anissa Cheriguene, Chaker Abdelaziz Kerrache, Carlos T. Calafate

引用次数: 0

A Deep Learning Approach for Malware Detection in IoT Binaries Using Spatial and Temporal Patterns 使用空间和时间模式的物联网二进制文件中恶意软件检测的深度学习方法

IF 0.9

Internet Technology Letters Pub Date : 2025-05-23 DOI: 10.1002/itl2.70032

M. Nandish, Jalesh Kumar

{"title":"A Deep Learning Approach for Malware Detection in IoT Binaries Using Spatial and Temporal Patterns","authors":"M. Nandish, Jalesh Kumar","doi":"10.1002/itl2.70032","DOIUrl":"https://doi.org/10.1002/itl2.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>The proliferation of malware in the Internet of Things (IoT) environment poses significant challenges to IoT security due to the heterogeneity and resource constraints of IoT devices. Traditional malware detection methods, which rely heavily on individual features, static analysis, and raw byte sequences, suffer from performance limitations and are not effective against evolving threats. The proposed work introduces a novel deep learning-based malware detection model that integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to learn spatial and temporal representations from binary features. CNN extracts spatial patterns from static binary representations, while GRU extracts sequential dependencies in dynamic binary features, enabling the detection of complex malware behaviors. To further enhance detection efficiency, a feature selection mechanism is incorporated to identify the most relevant spatial–temporal features, reducing training time while maintaining high detection accuracy. The proposed approach effectively combines static and dynamic feature representations to train a robust classifier capable of detecting sophisticated malware patterns. Experimental evaluations on an IoT malware dataset demonstrate the efficacy of the proposed model, achieving an average detection accuracy of 99.33%, significantly outperforming traditional methods. The results also show the model's robustness against obfuscation techniques, with a substantial reduction in the false positive rate (FPR).</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118151","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

Cloud-Edge Cooperation Mechanism for Fast Live Sports Video Distribution 体育视频直播快速分发的云边缘合作机制

IF 0.9

Internet Technology Letters Pub Date : 2025-05-20 DOI: 10.1002/itl2.70041

Jie Feng

{"title":"Cloud-Edge Cooperation Mechanism for Fast Live Sports Video Distribution","authors":"Jie Feng","doi":"10.1002/itl2.70041","DOIUrl":"https://doi.org/10.1002/itl2.70041","url":null,"abstract":"<div>\u0000 \u0000 <p>The proliferation of live video has led to an explosion of video content across cross-domain edge–cloud networks. This is particularly evident during intensive event coverage, where Sports Live imposes significant processing pressures on real-time delivery and user experience within these networks. To address these challenges, this paper introduces a fast video distribution system for sports content that leverages the synergy between cloud computing and edge computing. By deploying edge devices to distribute sports video content, the system adeptly manages the sparsity and randomness of user requests and behaviors in edge networks. Focusing on the characteristics of smaller user groups allows for a more accurate representation of the broader audience, optimizing performance at a lower operational cost. Both cloud and edge computing devices are equipped with storage capabilities to cache sports video content, thereby implementing a dual caching strategy. This approach offers two primary benefits: it conserves core network bandwidth and minimizes latency for users accessing content. Given the escalating demands for low-latency and high-bandwidth multimedia sports video content—such as real-time interactive sports broadcasts and UHD sports videos—the proposed cloud–edge collaborative caching mechanism effectively meets these stringent requirements. The system ensures seamless and efficient delivery, enhancing both user satisfaction and operational efficiency in dynamic sports streaming environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100801","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

Contrary Index Spectrum Allocations Scheme for Primary and Backup Routes in Survivable Mixed Grid Optical Networks 可生存混合网格光网络中主备路由反向索引频谱分配方案

IF 0.9

Internet Technology Letters Pub Date : 2025-05-08 DOI: 10.1002/itl2.70033

Dharmendra Singh Yadav

引用次数: 0

WDNet: An Underwater Acoustic Signal Denoising Algorithm Based on Wavelet Denoising and Deep Learning 基于小波去噪和深度学习的水声信号去噪算法

IF 0.9

Internet Technology Letters Pub Date : 2025-05-06 DOI: 10.1002/itl2.70022

Juan Li, Qingning Jia, Xuerong Cui, Lei Li, Bin Jiang, Shibao Li, Jianhang Liu

{"title":"WDNet: An Underwater Acoustic Signal Denoising Algorithm Based on Wavelet Denoising and Deep Learning","authors":"Juan Li, Qingning Jia, Xuerong Cui, Lei Li, Bin Jiang, Shibao Li, Jianhang Liu","doi":"10.1002/itl2.70022","DOIUrl":"https://doi.org/10.1002/itl2.70022","url":null,"abstract":"<div>\u0000 \u0000 <p>Modulation recognition in underwater acoustic (UWA) signals is challenging due to the intricate marine environment and substantial underwater noise. Wavelet-based denoising lacks adaptivity and can be affected by the wavelet function, the number of decomposition layers, and the threshold function. Although the denoising method based on deep learning has achieved a good denoising effect, it fails to integrate with the physical model and lacks certain theoretical support. To address these problems, this paper proposes a deep fusion network for signal denoising, named WDNet, based on wavelet denoising theory and deep learning techniques. We initialize the tap coefficients of the wavelet decomposition and reconstruction filters as learnable parameter matrices and use the soft threshold function as the activation function so as to realize the decomposition, thresholding, and reconstruction of the signal. The filter and threshold are adjusted adaptively by backpropagation to achieve optimal signal denoising. Simulation results demonstrate that our model achieves a higher signal-to-noise ratio (SNR) gain and lower root mean square error (RMSE) compared to other methods. After denoising, the recognition rate of UWA modulation signals significantly improves.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908937","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

Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles 提高车联网生存能力的区块链辅助信任框架

IF 0.9

Internet Technology Letters Pub Date : 2025-05-06 DOI: 10.1002/itl2.70029

Kanta Prasad Sharma, Atheer Al-Rashed, M. G. M. Johar, Anas Ratib Alsoud, Amrita Singh, Protyay Dey, Shivani Pant

{"title":"Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles","authors":"Kanta Prasad Sharma, Atheer Al-Rashed, M. G. M. Johar, Anas Ratib Alsoud, Amrita Singh, Protyay Dey, Shivani Pant","doi":"10.1002/itl2.70029","DOIUrl":"https://doi.org/10.1002/itl2.70029","url":null,"abstract":"<div>\u0000 \u0000 <p>VANETs enhance traffic efficiency and road safety, but they can be attacked by malicious vehicles. These malicious vehicles can cause accidents or endanger lives by broadcasting false event messages and disrupting Internet of Vehicles applications. Before responding to sender messages, receiver vehicles must estimate the legitimacy and trustworthiness of the source vehicles. Existing solutions struggle to balance security and efficiency effectively. This paper introduces a model that combines the advantages of blockchain technology and trust model models to improve the trustworthiness, efficacy, and security of vehicular networks, alongside secure trust-based optimized routing. Extensive experiments demonstrate the security and efficiency of the proposed model. The proposed model is adaptable to diverse VANET scenarios, addressing all security and privacy needs more comprehensively than current trust schemes. Efficiency analysis and simulation results show that our proposed framework outperforms baseline models, highlighting its security, effectiveness, and robustness in enhancing IoV communication security.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909058","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