RF-based Monitoring, Sensing and Localization of Mobile Wireless Nodes

被引:0
|
作者
Carvalho, Marco M. [1 ]
Hambebo, Bereket M. [1 ]
Granados, Adrian [1 ]
机构
[1] Florida Inst Technol, Sch Comp, Harris Inst Assured Informat, Melbourne, FL 32901 USA
关键词
NETWORK;
D O I
10.1007/978-3-319-52712-3_5
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Spectrum sensing and characterization play a very important role in the implementation of cognitive radios and adaptive mobile wireless networks. Most practical mobile network deployments require some level of sensing and adaptation to allow individual nodes to learn and reconfigure based on observations from their own environment. Spectrum sensing can be used for detection of a transmitter in a specific band, which can help cognitive radios to detect spectrum holes for secondary users and to determine the presence of a transmitter in a given area. In addition to determining the existence of a transmitter, information obtained from spectrum sensing can be used to localize a transmitter. In this paper, we focus in oner particular aspect o that problem: the distributed and collaborative sensing, characterization and location of emitters in an open environment. Thus, we propose a software defined radio (SDR)-based spectrum sensing and localization method. The proposed approach uses energy detection for spectrum sensing and fingerprinting techniques for estimating the location of the transmitter. A Universal Software Radio Peripheral (USRP) managed via a small, low-cost computer is used for spectrum sensing. Results obtained from an indoor experimental setup and the K-nearest neighbor algorithm for the fingerprinting based localization are presented in this paper.
引用
收藏
页码:61 / 71
页数:11
相关论文
共 50 条
  • [41] Node Localization Based on Improved PSO and Mobile Nodes for Environmental Monitoring WSNs
    Shen S.
    Sun L.
    Dang Y.
    Zou Z.
    Wang R.
    International Journal of Wireless Information Networks, 2018, 25 (04) : 470 - 479
  • [42] Rf-based fingerprinting for indoor localization: deep transfer learning approach
    Safwat R.
    Shaaban E.
    Al-Tabbakh S.M.
    Emara K.
    Journal of Ambient Intelligence and Humanized Computing, 2024, 15 (9) : 3393 - 3403
  • [43] The Case for Efficient and Robust RF-Based Device-Free Localization
    Xu, Chenren
    Firner, Bernhard
    Zhang, Yanyong
    Howard, Richard E.
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2016, 15 (09) : 2362 - 2375
  • [44] DeepVS: A Deep Learning Approach For RF-based Vital Signs Sensing
    Xie, Zongxing
    Wang, Hanrui
    Han, Song
    Schoenfeld, Elinor
    Ye, Fan
    13TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, BCB 2022, 2022,
  • [45] Corrosion monitoring in reinforced concrete structures with an innovative rf-based sensor
    Dressler, Inka
    Wichmann, Hans-Joachim
    Budelmann, Harald
    BAUTECHNIK, 2015, 92 (10) : 683 - 687
  • [46] A MPF Based Distributed Method for Mobile Robot and Wireless Sensor Nodes Simultaneous Localization
    Kong Liang
    Kong Lingfu
    Wu Peiliang
    Li Yuerong
    PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 4895 - 4899
  • [47] A localization algorithm in wireless sensor networks with mobile anchor nodes
    Shi, Ting-Jun
    Sang, Xia
    Xu, Li-Jie
    Yin, Xin-Chun
    Ruan Jian Xue Bao/Journal of Software, 2009, 20 (SUPPL. 1): : 278 - 285
  • [48] Hybrid localization algorithm of mobile nodes in wireless sensor networks
    Chen Y.
    Chen J.
    Wang Z.
    Ren T.
    Journal of Communications, 2016, 11 (04): : 358 - 364
  • [49] RF-Based Noncontact Respiratory Rate Monitoring With Parametric Spectral Estimation
    Uysal, Can
    Filik, Tansu
    IEEE SENSORS JOURNAL, 2019, 19 (21) : 9841 - 9849
  • [50] RF-based Inertial Measurement
    Wu, Chenshu
    Zhang, Feng
    Fan, Yusen
    Liu, K. J. Ray
    PROCEEDINGS OF THE 2019 ACM SIGCOMM CONFERENCE POSTERS AND DEMOS (SIGCOMM '19), 2019, : 78 - 79