Abstract
The paper presents a practical realisation of the localisation of a radio wave source. The system is based on the RSSI principle and uses a set of blind mobile agents. The main goal of the research was to implement the localisation system of the root node in WSN on the low-cost autonomous mobile embedded platform. This platform has a limited ability for complex mathematical operations, therefore more easy algorithms should be used. The study focused on implementation of movement algorithms and exchanging the knowledge between blind agents. In the solution described the gradient search path algorithm was implemented.
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
Han, G., Xu, H., Duong, T.Q., Jiang, J., Hara, T.: Localization algorithms of wireless sensor networks: a survey. Telecommun. Syst. 52(4), 2419–2436 (2013)
Xueqing, W., Shuxue, Z.: Comparison of several sensor deployments in wireless sensor networks. In: 2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies (EDT), vol. 1, pp. 236–239, April 2010
Mistry, H.P., Mistry, N.H.: RSSI based localization scheme in wireless sensor networks: a survey. In: Fifth International Conference on Advanced Computing and Communication Technologies, pp. 647–652, February 2015
Kwiecień, A., Maćkowski, M., Kojder, M., Manczyk, M.: Reliability of bluetooth smart technology for indoor localization system. In: Gaj, P., Kwiecień, A., Stera, P. (eds.) CN 2015. CCIS, vol. 522, pp. 444–454. Springer, Heidelberg (2015)
Capriglione, D., Ferrigno, L., D’Orazio, E., Paciello, V., Pietrosanto, A.: Reliability analysis of RSSI for localization in small scale WSNs. In: IEEE International Instrumentation and Measurement Technology Conference, pp. 935–940 (2012)
Bernas, M., Płaczek, B.: Energy aware object localization in wireless sensor network based on Wi-Fi fingerprinting. In: Gaj, P., Kwiecień, A., Stera, P. (eds.) CN 2015. CCIS, vol. 522, pp. 33–42. Springer, Heidelberg (2015)
Adewumi, O.G., Djouani, K., Kurien, A.: RSSI based indoor and outdoor distance estimation for localization in WSN. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1534–1539 (2013)
Larranaga, J., Muguira, L., Lopez-Garde, J., Vazquez, J.: An environment adaptive ZigBee-based indoor positioning algorithm. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2010)
Oliveira, L., Hongbin, H., Abrudan, T., Almeida, L.: RSSI-based relative localisation for mobile robots. Ad-Hoc Netw. 13(B), 321–335 (2014)
Kouril, J.: Using RSSI parameter in tracking methods - Practical test. In: 2011 34th International Conference on Telecommunications and Signal Processing (TSP), pp. 248–251, August 2011
Chaochen, W., Yongxin, Z.: An improved localization framework based on maximum likelihood for blind WSN nodes. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), pp. 1567–1572, August 2015
IEEE Standards Association: IEEE 802.15: Wireless Personal Area Networks (PANs). https://standards.ieee.org/about/get/802/802.15.html
Bedford, M.D., Kennedy, G.A.: Evaluation of ZigBee (IEEE 802.15.4) time-of-flight-based distance measurement for application in emergency underground navigation. IEEE Trans. Antennas Propag. 60(5), 2502–2510 (2012)
Elango, S., Mathivanan, N., Gupta, P.K.: RSSI based indoor position monitoring using WSN in a home automation application. Acta Electrotechnica et Informatica 11(4), 14–19 (2011)
Arexx: Arexx AAR-04 robot specification, December 2015. http://www.conrad.com/ce/en/product/191694/Arexx-AAR-04-Programmable-Arduino-Robot
Digi International: XBee and XBee-PRO ZB Modules – Datasheet (2011). https://cdn.sparkfun.com/datasheets/Wireless/Zigbee/ds_xbeezbmodules.pdf
Arduino: Arduino Uno board, September 2014. https://www.arduino.cc/en/Main/ArduinoBoardUno
Arduino: Arduino Software (IDE), September 2014. https://www.arduino.cc/en/Guide/Environment
Andrew Wrapp: Arduino Xbee Library, November 2015. https://github.com/andrewrapp/xbee-arduino
MacDonald, D.K.C.: Noise and Fluctuations: An Introduction. National Research Council, Ottawa (1962)
Sutton, S., Barto, G.: Reinforcement Learning: An Introduction, 2nd edn. The MIT Press, London (2012)
Itseez: OpenCV Library, January 2016. http://opencv.org/about.html
Kopniak, P.: The use of multiple cameras for motion capture. Przeglad Elektrotechniczny 90(4), 173–176 (2014)
Ozturk, T., Albayrak, Y., Polat, O.: Object tracking by PI control and image processing on embedded systems. In: 23th Signal Processing and Communications Applications Conference (SIU), pp. 2178–2181 (2015)
Shopa, P., Sumitha, N., Patra, P.S.K.: Traffic sign detection and recognition using OpenCV. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 1–6 (2014)
Kuehlkamp, A., Franco, C.R., Comunello, E.: An evaluation of iris detection methods for real-time video processing with low-cost equipment. In: Czachorski, T., Gelenbe, E., Lent, R. (eds.) Information Sciences and Systems, pp. 105–113. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Czerwinski, D., Przylucki, S., Mukharsky, D. (2016). RSSI-Based Localisation of the Radio Waves Source by Mobile Agents. In: Gaj, P., Kwiecień, A., Stera, P. (eds) Computer Networks. CN 2016. Communications in Computer and Information Science, vol 608. Springer, Cham. https://doi.org/10.1007/978-3-319-39207-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-39207-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39206-6
Online ISBN: 978-3-319-39207-3
eBook Packages: Computer ScienceComputer Science (R0)