Gaussian Mixture Model with Bayesian Approach for Maximizing RSS-based Localization in Underwater Wireless Sensor Networks
Main Article Content
Abstract
Source localization is a highly challenging and complex task in underwater environments due to uncertainties and unknown sound propagation speed profiles in underwater channels, as well as increased Doppler effects and constraints on the energy sources of the sensor nodes. To address these issues, we propose an energy-efficient Joint Gaussian Mixture Model with a Bayesian approach for localization algorithms, aiming to improve Received Signal Strength (RSS) accuracy. In this article, we represent the additive noise using a Gaussian Mixture Model to calculate the maximum likelihood estimation. The Bayesian statistical approach solves the convex optimization problem to find effective globally optimal solutions. These joint methods help mitigate the underwater Doppler spread effects and improve the estimation of sensor node positions. The simulated results are analyzed, and the performance metrics show that the proposed GMM-Bayesian approach is very close to the Cramér-Rao Lower Bound and this method also outperforms other existing localization algorithms in terms of lower Root Mean Squared Error (RMSE) relative to anchor nodes and a better Cumulative Distribution Function (CDF) for localization errors. From the simulation results, it is evident that the proposed approach achieves substantial performance gains in the localization of underwater wireless sensor networks.
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References
[1] Baranidharan, V., Moulieshwaran, B., Karthik, V., Sanjay, R., and Thangabalaji, V. Enhanced goodput and energy-efficient geo-opportunistic routing protocol for underwater wireless sensor networks. In Smart Computing Techniques and Applications: Proceedings of the Fourth International Conference on Smart Computing and Informatics, Volume 2, pages 585–593. Springer, 2021.
[2] Cario, G., Casavola, A., Gagliardi, G., Lupia, M., and Severino, U. Accurate localization in acoustic underwater localization systems. Sensors, 21(3):762, 2021.
[3] Chang, S., Li, Y., He, Y., and Wang, H. Target localization in underwater acoustic sensor networks using rss measurements. Applied Sciences, 8(2):225, 2018.
[4] Cheng, W., Teymorian, A. Y., Ma, L., Cheng, X., Lu, X., and Lu, Z. Underwater localization in sparse 3d acoustic sensor networks. In IEEE INFOCOM 2008-The 27th Conference on Computer Communications, pages 236–240. IEEE, 2008.
[5] Datta, A. and Dasgupta, M. On accurate localization of sensor nodes in underwater sensor networks: A doppler shift and modified genetic algorithm based localization technique. Evolutionary Intelligence, 14(1):119–131, 2021.
[6] De Palma, D., Arrichiello, F., Parlangeli, G., and Indiveri, G. Underwater localization using single beacon measurements: Observability analysis for a double integrator system. Ocean Engineering, 142:650–665, 2017.
[7] Fu, X., Fan, Z., Ling, M., Huang, Y., and Ding, X. Two-step approach for single underwater image enhancement. In 2017 international symposium on intelligent signal processing and communication systems (ISPACS), pages 789–794. Ieee, 2017.
[8] Ho, K., Lu, X., and Kovavisaruch, L.-o. Source localization using tdoa and fdoa measurements in the presence of receiver location errors: Analysis and solution. IEEE Transactions on Signal Processing, 55(2):684–696, 2007.
[9] Islam, K. Y., Ahmad, I., Habibi, D., and Waqar, A. A survey on energy efficiency in underwater wireless communications. Journal of Network and Computer Applications, 198:103295, 2022.
[10] Jia, T., Wang, H., Shen, X., and Yan, Y. Accurate closed-form solution for moving underwater vehicle localization using two-way travel time. Electronics, 9(4):565, 2020.
[11] Kaushal, H. and Kaddoum, G. Underwater optical wireless communication. IEEE access, 4:1518– 1547, 2016.
[12] Li, Q., Huang, Y., Song, X., Zhang, J., and Min, S. Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 214:129–138, 2019.
[13] Liu, Y., Wang, Y., Chen, C., and Liu, C. Underwater wireless sensor network-based localization method under mixed line-of-sight/non-lineof-sight conditions. Journal of Marine Science and Engineering, 11(9):1642, 2023.
[14] Luo, H., Zhao, Y., Guo, Z., Liu, S., Chen, P., and Ni, L. M. Udb: Using directional beacons for localization in underwater sensor networks. In 2008 14th IEEE International Conference on Parallel and Distributed Systems, pages 551–558. IEEE, 2008.
[15] Luo, J., Yang, Y., Wang, Z., and Chen, Y. Localization algorithm for underwater sensor network: A review. IEEE Internet of Things Journal, 8(17):13126–13144, 2021.
[16] Ma, C., Wang, L., Gao, J., Cui, Y., Peng, C., and Zhang, S. Time of arrival estimation for underwater acoustic signal using multi-feature fusion. Applied Acoustics, 211:109475, 2023.
[17] Mandic, F., Miškovi ´ c, N., and Lon ´ car, I. Un- ˇ derwater acoustic source seeking using timedifference-of-arrival measurements. IEEE Journal of Oceanic Engineering, 45(3):759–771, 2019.
[18] Moradi, H. R., Omidvar, M. E., Adil Khan, M., and Nikodem, K. Around jensens inequality for strongly convex functions. Aequationes mathematicae, 92:25–37, 2018.
[19] Pranitha, B. and Anjaneyulu, L. Review of research trends in underwater communications - a technical survey. In 2016 international conference on communication and signal processing (ICCSP), pages 1443–1447. IEEE, 2016.
[20] Ragavi, B., Baranidharan, V., John Clement Sunder, A., Pavithra, L., and Gokulraju, S. A comprehensive survey on different routing protocols and challenges in underwater acoustic sensor networks. Recent Advances in Metrology: Select Proceedings of AdMet 2021, pages 309–320, 2022.
[21] Ragavi, B., Baranidharan, V., and Ramash Kumar, K. A novel hybridized cluster-based geographical opportunistic routing protocol for effective data routing in underwater wireless sensor networks. Journal of Electrical and Computer Engineering, 2023(1):5567483, 2023.
[22] Rehan, K. and Qiao, G. A survey of underwater acoustic communication and networking techniques. Res. J. Appl. Sci. Eng. Technol, 5(3):778– 789, 2013.
[23] Sari, R. and Zayyani, H. Rss localization using unknown statistical path loss exponent model. IEEE Communications Letters, 22(9):1830–1833, 2018.
[24] Stojanovic, M. On the relationship between capacity and distance in an underwater acoustic communication channel. ACM SIGMOBILE Mobile Computing and Communications Review, 11(4):34–43, 2007.
[25] Stojanovic, M. and Preisig, J. Underwater acoustic communication channels: Propagation models and statistical characterization. IEEE communications magazine, 47(1):84–89, 2009.
[26] Su, X., Ullah, I., Liu, X., and Choi, D. A review of underwater localization techniques, algorithms, and challenges. Journal of Sensors, 2020(1):6403161, 2020.
[27] van Kleunen, W. A., Blom, K. C., Meratnia, N., Kokkeler, A. B., Havinga, P. J., and Smit, G. J. Underwater localization by combining time-offlight and direction-of-arrival. In OCEANS 2014- TAIPEI, pages 1–6. IEEE, 2014.
[28] Varadharajan, B., Gopalakrishnan, S., Varadharajan, K., Mani, K., and Kutralingam, S. Energy-efficient virtual infrastructure based geonested routing protocol forwireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences, 29(2):745–755, 2021.
[29] Villa, M., Ferreira, B., and Cruz, N. Genetic algorithm to solve optimal sensor placement for underwater vehicle localization with range dependent noises. Sensors, 22(19):7205, 2022.
[30] Wang, S. and Hu, H. Wireless sensor networks for underwater localization: A survey. 2012.
[31] Xu, C., Xu, C., Wu, C., Liu, J., Qu, D., and Xu, F. Accurate two-step filtering for auv navigation in large deep-sea environment. Applied Ocean Research, 115:102821, 2021.
[32] Yan, Y., Yang, G., Wang, H., and Shen, X. Semidefinite relaxation for source localization with quantized toa measurements and transmission uncertainty in sensor networks. IEEE Transactions on Communications, 69(2):1201–1213, 2020.
[33] Yin, F., Fritsche, C., Jin, D., Gustafsson, F., and Zoubir, A. M. Cooperative localization in wsns using gaussian mixture modeling: Distributed ecm algorithms. IEEE Transactions on Signal Processing, 63(6):1448–1463, 2015.
[34] Zeng, Z., Fu, S., Zhang, H., Dong, Y., and Cheng, J. A survey of underwater optical wireless communications. IEEE communications surveys & tutorials, 19(1):204–238, 2016.
[35] Zhang, H. Underwater sensor network nodes selflocalization in electronic technology. In Advances in Mechanical and Electronic Engineering: Volume 2, pages 535–540. Springer, 2012.
[36] Zhang, S., Zhang, K., Tian, C., Huang, J., and Shen, C. A three-step underwater moving target location algorithm based on fdoa. In 2022 6th International Conference on Wireless Communications and Applications (ICWCAPP), pages 1–5. IEEE, 2022.
[37] Zhang, Y., Xing, S., Zhu, Y., Yan, F., and Shen, L. Rss-based localization in wsns using gaussian mixture model via semidefinite relaxation. IEEE Communications Letters, 21(6):1329–1332, 2017.