International Journal of Wireless Information Networks最新文献_第7页

EARP: An Enhanced ACO-Based Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks EARP:多移动接收器无线传感器网络的增强型aco路由协议

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2022-01-21 DOI: 10.1007/s10776-021-00545-4

Noureddine Moussa, D. Benhaddou, Abdelbaki El Belrhiti El Alaoui

引用次数: 1

Spectrum Monitoring Based on End-to-End Learning by Deep Learning 基于端到端深度学习的频谱监测

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2022-01-12 DOI: 10.1007/s10776-021-00548-1

Mahdiyeh Rahmani, R. Ghazizadeh

引用次数: 0

Simulation and Modeling for Anomaly Detection in IoT Network Using Machine Learning 基于机器学习的物联网网络异常检测仿真与建模

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2022-01-05 DOI: 10.1007/s10776-021-00542-7

I. Mukherjee, N. K. Sahu, S. Sahana

引用次数: 5

Understanding of RF Cloud Interference Measurement and Modeling. 了解射频云干扰测量和建模。

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2022-01-01 Epub Date: 2021-12-18 DOI: 10.1007/s10776-021-00541-8

Kaveh Pahlavan

{"title":"Understanding of RF Cloud Interference Measurement and Modeling.","authors":"Kaveh Pahlavan","doi":"10.1007/s10776-021-00541-8","DOIUrl":"https://doi.org/10.1007/s10776-021-00541-8","url":null,"abstract":"Importance of spectrum regulation and management was first revealed on May of 1985 after the release of unlicensed ISM bands resulting in emergence of Wi-Fi, Bluetooth and many other wireless technologies that has affected our daily lives by enabling the emergence of the smart world and IoT era. Today, the idea of a liberated spectrum is circulating around, which can potentially direct wireless networking industry into another revolution by enabling a new paradigm in intelligent spectrum regulation and management. The RF signal radiated from IoT devices as well as other wireless technologies create an RF cloud causing co- and cross-interference to each other. Lack of a science and technology for understanding, measurement, and modeling of the RF cloud interference in near real-time results in inefficient utilization of the precious spectrum, a unique natural resource shared among all wireless devices of the universe in frequency, time, and space. Near real time forecasting of the RF cloud interference is essential to pursue the path to the optimal utilization of spectrum and a liberated spectrum management. This paper presents a historical perspective on the evolution of spectrum regulation and management, explains the diversified meanings of interference for different sectors of the wireless industry, and presents a path for implementing a theoretical foundation for interference monitoring and forecasting to enable the emergence of a liberated spectrum industry and a new paradigm in spectrum management and regulations.","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":"29 3","pages":"206-221"},"PeriodicalIF":2.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8683812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39765003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI. 流行病期间的接近检测:直接超宽带TOA与基于机器学习的RSSI。

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2022-01-01 Epub Date: 2022-10-14 DOI: 10.1007/s10776-022-00577-4

Zhuoran Su, Kaveh Pahlavan, Emmanuel Agu, Haowen Wei

{"title":"Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI.","authors":"Zhuoran Su, Kaveh Pahlavan, Emmanuel Agu, Haowen Wei","doi":"10.1007/s10776-022-00577-4","DOIUrl":"https://doi.org/10.1007/s10776-022-00577-4","url":null,"abstract":"In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.Graphical abstract: ","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":"29 4","pages":"480-490"},"PeriodicalIF":2.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40340023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network 基于时空融合的无线传感器网络寻源路径规划

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2021-11-20 DOI: 10.1007/s10776-021-00540-9

Cheng Xu, Jiawei Rong, Yulin Chen, Hang Wu, Shihong Duan

引用次数: 2

Blind Detection Design for AF Two-Way Relaying Over Frequency Selective Channels 频率选择信道上AF双向中继的盲检测设计

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2021-09-18 DOI: 10.1007/s10776-021-00536-5

Yanwu Ding, Lun Li

引用次数: 0

A CS-Based Grant-Free Media Access Scheme for NOMA-Based Industrial IoTs with Location Awareness 基于位置感知的基于noma的工业物联网中基于cs的免授权媒体访问方案

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2021-09-05 DOI: 10.1007/s10776-021-00527-6

Ruixia Li, Wei Peng, Chenxi Zhang

引用次数: 1

Vehicle Artificial Intelligence System Based on Intelligent Image Analysis and 5G Network 基于智能图像分析和5G网络的车辆人工智能系统

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2021-09-03 DOI: 10.1007/s10776-021-00535-6

B. Liu, Chen Han, Xinxin Liu, Wei Li

引用次数: 15

Human Body Communication In-Vivo Measurement Using Different Test Equipment 使用不同测试设备进行人体通信体内测量

IF 2.5

International Journal of Wireless Information Networks Pub Date : 2021-09-03 DOI: 10.1007/s10776-021-00534-7

Fouad Maamir, R. Touhami, S. Tedjini, M. Guiatni

引用次数: 0