{"title":"基于接收信号强度回归的随机 MAC 地址 BLE 广告数据包的设备识别方法","authors":"Shuhei Akiyama;Yoshiaki Taniguchi","doi":"10.23919/comex.2023XBL0157","DOIUrl":null,"url":null,"abstract":"In this letter, we propose a device identification method from observed Bluetooth Low Energy (BLE) advertising packets for tracking BLE devices even if their MAC addresses are changed periodically and randomly. In our proposed method, the combination of MAC addresses is formulated as a linear assignment problem. In addition, in a cost function of linear assignment, we combine two types of cost: time-based cost and received signal strength-based cost, which is calculated based on regression of received signal strength. Through experimental evaluations, we confirmed that the accuracy of our proposed method is the highest compared to traditional methods.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"13 3","pages":"64-67"},"PeriodicalIF":0.3000,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400563","citationCount":"0","resultStr":"{\"title\":\"A device identification method from BLE advertising packets with randomized MAC addresses based on regression of received signal strength\",\"authors\":\"Shuhei Akiyama;Yoshiaki Taniguchi\",\"doi\":\"10.23919/comex.2023XBL0157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this letter, we propose a device identification method from observed Bluetooth Low Energy (BLE) advertising packets for tracking BLE devices even if their MAC addresses are changed periodically and randomly. In our proposed method, the combination of MAC addresses is formulated as a linear assignment problem. In addition, in a cost function of linear assignment, we combine two types of cost: time-based cost and received signal strength-based cost, which is calculated based on regression of received signal strength. Through experimental evaluations, we confirmed that the accuracy of our proposed method is the highest compared to traditional methods.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"13 3\",\"pages\":\"64-67\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400563\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10400563/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10400563/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
在这封信中,我们提出了一种从观测到的蓝牙低功耗(BLE)广告数据包中进行设备识别的方法,即使 BLE 设备的 MAC 地址周期性地随机变化,也能对其进行跟踪。在我们提出的方法中,MAC 地址的组合被表述为一个线性分配问题。此外,在线性分配的成本函数中,我们结合了两种类型的成本:基于时间的成本和基于接收信号强度的成本,后者是根据接收信号强度的回归计算得出的。通过实验评估,我们证实与传统方法相比,我们提出的方法具有最高的准确性。
A device identification method from BLE advertising packets with randomized MAC addresses based on regression of received signal strength
In this letter, we propose a device identification method from observed Bluetooth Low Energy (BLE) advertising packets for tracking BLE devices even if their MAC addresses are changed periodically and randomly. In our proposed method, the combination of MAC addresses is formulated as a linear assignment problem. In addition, in a cost function of linear assignment, we combine two types of cost: time-based cost and received signal strength-based cost, which is calculated based on regression of received signal strength. Through experimental evaluations, we confirmed that the accuracy of our proposed method is the highest compared to traditional methods.