Pub Date : 2022-01-12DOI: 10.1007/s10776-021-00548-1
Mahdiyeh Rahmani, R. Ghazizadeh
{"title":"Spectrum Monitoring Based on End-to-End Learning by Deep Learning","authors":"Mahdiyeh Rahmani, R. Ghazizadeh","doi":"10.1007/s10776-021-00548-1","DOIUrl":"https://doi.org/10.1007/s10776-021-00548-1","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49127659","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}
Pub Date : 2022-01-05DOI: 10.1007/s10776-021-00542-7
I. Mukherjee, N. K. Sahu, S. Sahana
{"title":"Simulation and Modeling for Anomaly Detection in IoT Network Using Machine Learning","authors":"I. Mukherjee, N. K. Sahu, S. Sahana","doi":"10.1007/s10776-021-00542-7","DOIUrl":"https://doi.org/10.1007/s10776-021-00542-7","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43090563","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}
Pub Date : 2022-01-01Epub Date: 2021-12-18DOI: 10.1007/s10776-021-00541-8
Kaveh Pahlavan
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.
{"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":"<p><p>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.</p>","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"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}
Pub Date : 2022-01-01Epub Date: 2022-10-14DOI: 10.1007/s10776-022-00577-4
Zhuoran Su, Kaveh Pahlavan, Emmanuel Agu, Haowen Wei
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.
{"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":"<p><p>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.</p><p><strong>Graphical abstract: </strong></p>","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"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}
Pub Date : 2021-11-20DOI: 10.1007/s10776-021-00540-9
Cheng Xu, Jiawei Rong, Yulin Chen, Hang Wu, Shihong Duan
{"title":"Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network","authors":"Cheng Xu, Jiawei Rong, Yulin Chen, Hang Wu, Shihong Duan","doi":"10.1007/s10776-021-00540-9","DOIUrl":"https://doi.org/10.1007/s10776-021-00540-9","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47227967","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}
Pub Date : 2021-09-18DOI: 10.1007/s10776-021-00536-5
Yanwu Ding, Lun Li
{"title":"Blind Detection Design for AF Two-Way Relaying Over Frequency Selective Channels","authors":"Yanwu Ding, Lun Li","doi":"10.1007/s10776-021-00536-5","DOIUrl":"https://doi.org/10.1007/s10776-021-00536-5","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42797320","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}
Pub Date : 2021-09-05DOI: 10.1007/s10776-021-00527-6
Ruixia Li, Wei Peng, Chenxi Zhang
{"title":"A CS-Based Grant-Free Media Access Scheme for NOMA-Based Industrial IoTs with Location Awareness","authors":"Ruixia Li, Wei Peng, Chenxi Zhang","doi":"10.1007/s10776-021-00527-6","DOIUrl":"https://doi.org/10.1007/s10776-021-00527-6","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48433830","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}
Pub Date : 2021-09-03DOI: 10.1007/s10776-021-00535-6
B. Liu, Chen Han, Xinxin Liu, Wei Li
{"title":"Vehicle Artificial Intelligence System Based on Intelligent Image Analysis and 5G Network","authors":"B. Liu, Chen Han, Xinxin Liu, Wei Li","doi":"10.1007/s10776-021-00535-6","DOIUrl":"https://doi.org/10.1007/s10776-021-00535-6","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48113176","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}
Pub Date : 2021-09-03DOI: 10.1007/s10776-021-00534-7
Fouad Maamir, R. Touhami, S. Tedjini, M. Guiatni
{"title":"Human Body Communication In-Vivo Measurement Using Different Test Equipment","authors":"Fouad Maamir, R. Touhami, S. Tedjini, M. Guiatni","doi":"10.1007/s10776-021-00534-7","DOIUrl":"https://doi.org/10.1007/s10776-021-00534-7","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45563310","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}
Pub Date : 2021-08-24DOI: 10.1007/s10776-021-00528-5
Z. Dong
{"title":"Research on Grid Connected Control Method of Single Phase Inverter Based on Wireless Sensor Network","authors":"Z. Dong","doi":"10.1007/s10776-021-00528-5","DOIUrl":"https://doi.org/10.1007/s10776-021-00528-5","url":null,"abstract":"","PeriodicalId":45393,"journal":{"name":"International Journal of Wireless Information Networks","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10776-021-00528-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41684334","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}
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