IEEE Networking Letters最新文献_第4页

Bluetooth Low Energy-Based Novel Power Efficient Buffalo Calving Detection Solution 基于蓝牙低功耗的新型高能效水牛产犊检测解决方案

IEEE Networking Letters Pub Date : 2025-01-07 DOI: 10.1109/LNET.2025.3526658

Radhika Raina;Kamal Jeet Singh;Suman Kumar;Sarthak Jain

{"title":"Bluetooth Low Energy-Based Novel Power Efficient Buffalo Calving Detection Solution","authors":"Radhika Raina;Kamal Jeet Singh;Suman Kumar;Sarthak Jain","doi":"10.1109/LNET.2025.3526658","DOIUrl":"https://doi.org/10.1109/LNET.2025.3526658","url":null,"abstract":"This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is designed for buffalo comfort and can be conveniently attached to the tail. The device wirelessly transmits data to a gateway using Bluetooth Low Energy (BLE). This functionality becomes particularly crucial when the tail movement increases in the last 12 hrs before calving and is regarded as a key behavioral indicator for predicting the onset of labor. Moreover, when an accelerometer is tied to the buffalo’s tail, the Z axis, which represents the vertical axis perpendicular to ground is anticipated to show the most notable deflections during this period as discussed in the literature. Thus, to conserve power, data is only transmitted when significant tail movement is detected, typically 12 hrs before calving, i.e., when Mean Z >-3 m/<inline-formula> <tex-math>$s^{2}$ </tex-math></inline-formula>. This approach reduces the device’s power consumption, extending its battery life to more than 6.08 years (approx.) using 620 mAh / 3V battery.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645266","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

Individual Packet Features are a Risk to Model Generalization in ML-Based Intrusion Detection 在基于机器学习的入侵检测中，单个数据包特征对模型泛化存在风险

IEEE Networking Letters Pub Date : 2025-01-03 DOI: 10.1109/LNET.2025.3525901

Kahraman Kostas;Mike Just;Michael A. Lones

引用次数: 0

Quantum-Safe Blockchain in Hyperledger Fabric 超级账本结构中的量子安全区块链

IEEE Networking Letters Pub Date : 2024-12-26 DOI: 10.1109/LNET.2024.3522966

Shahroz Abbas;Ajmery Sultana;Georges Kaddoum

引用次数: 0

HeRo: Heuristic-Based Routing in Payment Channel Networks 英雄：支付通道网络中基于启发式的路由

IEEE Networking Letters Pub Date : 2024-12-19 DOI: 10.1109/LNET.2024.3520350

Shruti Mishra;Virat Aggarwal;Sujata Pal;Vidushi Agarwal

引用次数: 0

SAcache: A Strongly Adaptive Online Caching Scheme for Non-Stationary Environments SAcache：一种非平稳环境下的强自适应在线缓存方案

IEEE Networking Letters Pub Date : 2024-12-11 DOI: 10.1109/LNET.2024.3516321

Zhenghao Sha;Kechao Cai;Jinbei Zhang

{"title":"SAcache: A Strongly Adaptive Online Caching Scheme for Non-Stationary Environments","authors":"Zhenghao Sha;Kechao Cai;Jinbei Zhang","doi":"10.1109/LNET.2024.3516321","DOIUrl":"https://doi.org/10.1109/LNET.2024.3516321","url":null,"abstract":"Online caching at the network edge is becoming increasingly important for alleviating the transmission pressure on backbone networks. Previous studies on online caching policies mainly use the static regret as the performance metric, which relies on a fixed benchmark and lacks the capacity to ensure optimal performance in non-stationary environments. In this letter, we introduce the strongly adaptive regret into online caching and propose a Strongly Adaptive online caching scheme (SAcache). Our SAcache scheme focuses on the performance over time intervals with a length between <inline-formula> <tex-math>$tau _{min }$ </tex-math></inline-formula> and <inline-formula> <tex-math>$tau _{max }$ </tex-math></inline-formula>, where <inline-formula> <tex-math>$tau _{min }$ </tex-math></inline-formula> and <inline-formula> <tex-math>$tau _{max }$ </tex-math></inline-formula> are the lower and upper bound on how long the environment changes, respectively. SAcache consists of multiple interval caches operating in a lazy restart mode to make candidate caching decisions, and an aggregated cache that weights the these candidate decisions to determine the final caching decision. We prove that the regret upper bound is sub-linear with respect to the time interval’s length <inline-formula> <tex-math>$tau $ </tex-math></inline-formula>, i.e., <inline-formula> <tex-math>$O(sqrt {tau log (tau _{max }/tau _{min })})$ </tex-math></inline-formula>. Our experiment results demonstrate that SAcache achieves the highest cache hit ratio and the lowest regret compared to other caching policies in non-stationary environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"46-50"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645191","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

FBCNet: Fusion Basis Complex-Valued Neural Network for CSI Compression in Massive MIMO Networks 大规模MIMO网络中CSI压缩的融合基复值神经网络

IEEE Networking Letters Pub Date : 2024-12-09 DOI: 10.1109/LNET.2024.3512658

C Kiruthika;E. S. Gopi

{"title":"FBCNet: Fusion Basis Complex-Valued Neural Network for CSI Compression in Massive MIMO Networks","authors":"C Kiruthika;E. S. Gopi","doi":"10.1109/LNET.2024.3512658","DOIUrl":"https://doi.org/10.1109/LNET.2024.3512658","url":null,"abstract":"Deep learning-based CSI compression has shown its efficacy for massive multiple-input multiple-output networks, and on the other hand, federated learning (FL) excels the conventional centralized learning by avoiding privacy leakage issues and training communication overhead. The realization of an FL-based CSI feedback network consumes more computational resources and time, and the continuous reporting of local models to the base station results in overhead. To overcome these issues, in this letter, we propose a FBCNet. The proposed FBCNet combines the advantages of the novel fusion basis (FB) technique and the fully connected complex-valued neural network (CNet) based on gradient (G) and non-gradient (NG) approaches. The experimental results show the advantages of both CNet and FB individually over the existing techniques. FBCNet, the combination of both FB and CNet, outperforms the existing federated averaging-based CNet (FedCNet) with improvement in reconstruction performance, less complexity, reduced training time, and low transmission overhead. For the distributed array-line of sight topology at the compression ratio (CR) of 20:1, it is noted that the NMSE and the cosine similarity of FedCNet-G are −8.2837 dB, 0.9262; FedCNet-NG are −3.5291 dB, 0.8452; proposed FB are −26.8621, 0.9653. Also the NMSE and the cosine similarity of the proposed FBCNet-G are −19.7521, 0.9307; FBCNet-NG are −24.0442, 0.9539 at a high CR of 64:1.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"262-266"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388486","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

Zero Trust Security Architecture for 6G Open Radio Access Networks (ORAN) 面向6G开放无线接入网络（ORAN）的零信任安全架构

IEEE Networking Letters Pub Date : 2024-12-09 DOI: 10.1109/LNET.2024.3514357

Hajar Moudoud;Zakaria Abou El Houda;Bouziane Brik

引用次数: 0

AI-Centric D2D in 6G Networks 6G网络中以ai为中心的D2D

IEEE Networking Letters Pub Date : 2024-12-09 DOI: 10.1109/LNET.2024.3512659

Jianwen Xu;Kaoru Ota;Mianxiong Dong

引用次数: 0

Serve Yourself! Federated Power Control for AI-Native 5G and Beyond 为你自己!AI-Native 5G及以后的联合功率控制

IEEE Networking Letters Pub Date : 2024-12-02 DOI: 10.1109/LNET.2024.3509792

Saad Abouzahir;Essaid Sabir;Halima Elbiaze;Mohamed Sadik