Abstract
RSSI technology has no additional hardware support, low energy consumption and low cost, but it has poor adaptability in different environments which would result in large errors when mapping RSSI signal to the measurement distance between nodes directly. In order to improve localization accuracy of Wireless Sensor Network, we propose a Centroid Localization based on Fuzzy Clustering and Data Consistency. Firstly, the measurement distance is preprocessed, and the anchor node with the largest received signal strength is found as the reference node to eliminate the measurement error within communication range of unknown nodes. Secondly, Fuzzy Clustering and Data Consistency are used to remove the coarse error. Finally, the improved Weighted Centroid algorithm is used to locate unknown nodes. The simulation results show that the FCDC-CL algorithms average localization error is approximately 9.4\(\%\) and the error is significantly reduced compared with the traditional WCL algorithm.
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Zhan, J., Liu, H.L., Tan, J.: Research on ranging accuracy based on RSSI of wireless sensor network. In: International Conference on Information Science and Engineering, Hangzhou, China, pp. 2338–2341. IEEE (2010)
Zaidi, S., Assaf, A., Affes, S., et al.: Range-free node localization in multi-hop wireless sensor networks. In: Wireless Communications and Networking Conference, Doha, Qatar. IEEE (2016)
Roos, T., Myllymki, P., Tirri, H., et al.: A probabilistic approach to WLAN user location estimation. Int. J. Wirel. Inf. Netw. 9(3), 155–164 (2002)
Youssef, M., Agrawala, A., Udaya, S.: WLAN location determination via clustering and probability distributions. In: IEEE International Conference on Pervasive Computing and Communications, Fort Worth, TX, USA, pp. 143–150. IEEE (2003)
Fang, Z., Zhao, Z., Guo, P., et al.: Analysis of distance measurement based on RSSI. Chin. J. Sens. Actuators 20(3), 2526–2530 (2007)
Liu, Z.: Error self-calibration localization algorithm based on RSSI. Chin. J. Sens. Actuators 26(7), 970–975 (2014)
Blumenthal, J., Grossmann, R., Golatowski, F., et al.: Weighted centroid localization in Zigbee-based sensor networks. In: IEEE International Symposium on Intelligent Signal Processing, Alcala de Henares, Spain, pp. 1–6. IEEE (2007)
Yu, X., Zhou, L., Zhang, F., et al.: Weight optimized centroid localization algorithm on radioactive pollution monitoring by WSN for uranium tailings. In: IEEE International Symposium on Intelligent Signal Processing, Beijing, China, pp. 135–138. IEEE (2016)
Sun, D., Qian, Z., Han, M., et al.: Improving multilateration algorithm by cluster analysis in WSN. Acta Electronic Sinica 42(8), 1601–1607 (2014)
Zhang, C., Gu, Y.: Cluster analysis based and threshold based selection localization algorithm for WSN. In: International Conference on Electronics Information and Emergency Communication, Beijing, China, pp. 186–189. IEEE (2015)
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Xue, S., Li, M., Yang, P. (2018). Centroid Location Technology Based on Fuzzy Clustering and Data Consistency. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_13
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DOI: https://doi.org/10.1007/978-3-030-00018-9_13
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