Abstract
In wireless sensor network (WSN), localization has a vital role to improve the performance of sensor networks. The proposed fuzzy optimization technique determines the sensor node location in an efficient manner. The weights can be evaluated on the basis of received signal strength indicator (RSSI)-based Mamdani fuzzy inference system. To find the location of the un-localized node, centroid-based technique is proposed. The proposed efficient fuzzy method is represented for a sensor network, and the simulation result gives direction and enhanced the performance of the wireless sensor network. The above said method is an optimistic one for getting the location of the sensors with zero or less error in contrast to simple weighted centroid technique.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tubaishat, M., Peng, Z., Qi, Q., Yi, S.: Wireless sensor networks in intelligent transportation systems. Wireless Communication and Mobile Computing. (2009), Vol. 9, 287–302.
Aspnes, J., Eren, T., Goldenberg, D.K., Morse, A.S., Whiteley, W., Yang, Y.Y, Anderson, B.D., Belhumeur, P.N.: A theory of network localization. IEEE Transactions on Mobile Computing. (2006), vol. 5, No. 12, 1663–1678.
Zheng, K., Wang, H., Li, H., Xiang, W., Lei, L., Qiao, J., & Shen, X. S. (2017). Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 66(3), 2714–2726.
Ren, W.: A rapid acquisition algorithm of WSN-aided GPS location. Proc. Int. Symp. Intell. Inf. Technol. Secur. Informatics, IITSI. (2009), 42–46.
Larios, D.F., Barbancho, J., Molina, F. J., Leon, C.: Locating sensors with fuzzy logic algorithms. IEEE Workshop On Merging Fields of Computational Intelligence and Sensor Technology—CompSens. (2011), 57–64.
Mustafa, A. M., Reza, A., Sener, U.: Range Free Localization of Wireless Sensor Networks Based on Sugeno Fuzzy Inference., SENSORCOMM August, (2012), 36–41.
Chuanhui, H., Zhan, X., Xiu, L. R.: Analysis and Improvement for MDS Localization Algorithm. IEEE. (2012). 12–15.
Velimirovic, A. S., Djordjevic, G. LJ., Velimirovic, M. M., Jovanovic, M. D.: A Fuzzy Set-Based Approach to Range-Free Localization in Wireless Sensor Networks. Facta Univ. Ser.: Elec. Energ., August (2010), vol. 23, no. 2, 227–244.
Sukhyun, Y., Jaehun, L., Wooyong, C., Euntai, K.: Centroid Localization Method in Wireless Sensor Networks using TSK Fuzzy Modeling, IEEE. 2009, 639–642.
Vasim B. M., Ramprasad, A.V.: Discrete Antithetic Markov Monte Carlo based Power Mapping Localization Algorithm for WSN, IEEE. (2012), 56–62.
Runjie, L., Kai, S., Jinyuan, S.: BP localization algorithm based on virtual nodes in wireless sensor network, IEEE. (2010), 1–4.
Tian, S., Zhang, X., Liu, P., Sun, P., Wang, X.: A RSSI-based DV-hop Algorithm for Wireless Sensor Networks. IEEE. (2007), 2555–2558.
Ding, Y., Tian, H., Han, G.: A Distributed Node Localization Algorithm for Wireless Sensor Network Based on MDS and SDP. Proceeding of International Conference on Computer Science and Electronics Engineering. (2012). 624–628.
Chaurasiya, V.K., Jain, N., Nandi, G.C.: A novel distance estimation approach for 3D localization in wireless sensor network using multi dimensional scaling. Information Fusion, vol. 15, no. 1, 5–18, (2014).
Slavisa, T., Marko, B., Rui, D., Goran, D., Milan T.: Distributed {RSS}-Based Localization in Wireless Sensor Networks with Node Selection Mechanism. Doctoral Conference on Computing, Electrical and Industrial Systems DoCEIS, vol. 450, 204–214, (2015).
Frankie, K. W. Chan., So, H.C., Ma, W.K.: A novel subspace approach for Co-operative Localization in Wireless Sensor Networks using Range measurements. IEEE Transactions on Signal Processing, IEEE Computer society. (2009), vol. 57, no. 1, 260–269.
Zeng, JI., Wang, H. Jin.: Improvement on APIT localization algorithm for Wireless Sensor networks. IEEE international conference on network security, wireless communication and trusted computing. (2011). 190–195.
Hamdoun, S., Rachedi, A., Benslimane, A.: RSSI-based Localization Algorithms using Spatial Diversity in Wireless Sensor Networks. International Journal of Ad Hoc and Ubiquitous Computing. Inderscience, vol. 19, no. 3, 157–167, (2015).
Jain, A., Ramana Reddy, B.V.: A Novel Method of Modeling Wireless Sensor Network Using Fuzzy Graph and Energy Efficient Fuzzy Based k-Hop Clustering Algorithm. Wireless Personal Communications, vol. 82, no. 1, pp. 157–181, (2015).
Xiaoyan, Li., Martin, R. P., Elnahrawy, E.: The Limits of Localization Using Signal Strength: A Comparative Study, SECON. (2004), 406–414.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rout, S.K., Rath, A.K., Mohapatra, P.K., Jena, P.K., Swain, A. (2018). A Fuzzy Optimization Technique for Energy Efficient Node Localization in Wireless Sensor Network Using Dynamic Trilateration Method. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_32
Download citation
DOI: https://doi.org/10.1007/978-981-10-7871-2_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7870-5
Online ISBN: 978-981-10-7871-2
eBook Packages: EngineeringEngineering (R0)