{"title":"Proactive Eavesdropping via Jamming for Trajectory Tracking of UAVs","authors":"Kai Li, S. Kanhere, Wei Ni, E. Tovar, M. Guizani","doi":"10.1109/IWCMC.2019.8766696","DOIUrl":null,"url":null,"abstract":"This paper considers that a legitimate UAV tracks suspicious UAVs’ flight for preventing intended crimes and terror attacks. To enhance tracking accuracy, the legitimate UAV proactively eavesdrops suspicious UAVs’ communication via sending jamming signals. A tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter’s signal. A new co-simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in Matlab) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of tracking accuracy of the suspicious UAVs’ trajectory.","PeriodicalId":363800,"journal":{"name":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWCMC.2019.8766696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
This paper considers that a legitimate UAV tracks suspicious UAVs’ flight for preventing intended crimes and terror attacks. To enhance tracking accuracy, the legitimate UAV proactively eavesdrops suspicious UAVs’ communication via sending jamming signals. A tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter’s signal. A new co-simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in Matlab) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of tracking accuracy of the suspicious UAVs’ trajectory.