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":null,"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":"29 4","pages":"480-490"},"PeriodicalIF":1.5000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562079/pdf/","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Wireless Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10776-022-00577-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/10/14 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 2
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.
期刊介绍:
International Journal of Wireless Information Networks is an international forum for the dissemination of knowledge related to wireless information networks for researchers in the telecommunications and computer industries. This outstanding quarterly publishes high-quality, peer-reviewed original papers on applications such as sensor and mobile ad-hoc networks, wireless personal area networks, wireless LANs, mobile data networks, location aware networks and services, and RF localization and RFID techniques. The journal also covers performance-predictions methodologies, radio propagation studies, modulation and coding, multiple access methods, security and privacy considerations, antenna and RF subsystems, VLSI and ASIC design, experimental trials, traffic and frequency management, and network signaling and architecture.
Four categories of papers are published: invited openings (review current and future directions), overview reports (address the philosophy and technical details of the standards and field trials), technical papers (present specific technical contributions of archival value), and letters (present new enhancement of previously published works, statements of open problems, comments on published papers, and corrections). International Journal of Wireless Information Networks aims to fill the needs of academic researchers involved in basic research at universities or research laboratories; telecommunications and computer engineers involved in design, planning, operation, and maintenance of state-of-the-art wireless information networks; and the technical community in telecommunications and computers involved in applied research and standards activities.
To view cumulative tables of contents, find details on the latest call for papers, or other information, please visit the http://www.cwins.wpi.edu/journal.html International Journal of Wireless Information Networks Web Site.