Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akyildiz, I.F., Lee, W.-Y., Vuran, M.C., Mohanty, S.: Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13), 2127–2159 (2006)
Wang, W.: Spectrum sensing for cognitive radio. In: Intelligent Information Technology Application Workshops, pp. 410–412 (2009)
Qiu, R.C., Zhang, C., Hu, Z., Wicks, M.C.: Towards a large-scale cognitive radio network testbed: spectrum sensing, system architecture, and distributed sensing. J. Commun. 7(7), 552–566 (2012)
Sahai, A., Hoven, N., Mishra, S.M., Tandra, R.: Fundamental tradeoffs in robust spectrum sensing for opportunistic frequency reuse. Submitted IEEE. J. Select. Areas Commun. 1 (2006)
Ye, Z., Grosspietsch, J., Memik, G.: Spectrum sensing using cyclostationary spectrum density for cognitive radios. In: 2007 IEEE Workshop on Signal Processing Systems, pp. 1–6. IEEE (2007)
Kim, K., Akbar, I., Bae, K., Um, J.-S., Spooner, C., Reed, J.: Cyclostationary approaches to signal detection and classification in cognitive radio. In: 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2007, pp. 212–215. IEEE (2007)
Ettus, M.: Universal software radio peripheral (USRP). Ettus Research LLC http://www.ettus.com
Digham, F.F., Alouini, M.-S., Simon, M.K.: On the energy detection of unknown signals over fading channels. In: IEEE International Conference on Communications, ICC 2003, vol. 5, pp. 3575–3579. IEEE (2003)
Sayed, A.H., Tarighat, A., Khajehnouri, N.: Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Process. Mag. 22(4), 24–40 (2005)
Fang, S.-H., Lin, T.-N.: Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments. IEEE Trans. Neural Netw. 19(11), 1973–1978 (2008)
Pahlavan, K., Li, X., Makela, J.-P.: Indoor geolocation science and technology. IEEE Commun. Mag. 40(2), 112–118 (2002)
Zhang, D., Xia, F., Yang, Z., Yao, L., Zhao, W.: Localization technologies for indoor human tracking. In: 5th International Conference on Future Information Technology (FutureTech), pp. 1–6. IEEE (2010)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 37(6), 1067–1080 (2007)
Ciurana, M., Barcelo-Arroyo, F., Izquierdo, F.: A ranging method with IEEE 802.11 data frames for indoor localization. In: Wireless Communications and Networking Conference, WCNC 2007, pp. 2092–2096. IEEE (2007)
Ladd, A.M., Bekris, K.E., Marceau, G., Rudys, A., Wallach, D.S., Kavraki, E.: Using wireless ethernet for localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 402–408. IEEE (2002)
Narzullaev, A., Park, Y., Jung, H.: Accurate signal strength prediction based positioning for indoor WLAN systems. In: Position, Location and Navigation Symposium, 2008 IEEE/ION, pp. 685–688. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Carvalho, M.M., Hambebo, B.M., Granados, A. (2017). RF-based Monitoring, Sensing and Localization of Mobile Wireless Nodes. In: Agüero, R., Zaki, Y., Wenning, BL., Förster, A., Timm-Giel, A. (eds) Mobile Networks and Management. MONAMI 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 191. Springer, Cham. https://doi.org/10.1007/978-3-319-52712-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-52712-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52711-6
Online ISBN: 978-3-319-52712-3
eBook Packages: Computer ScienceComputer Science (R0)