Vol. 12  No. 1 (2026) · Research Article

Improved Clustering Method for Wireless Sensor Networks Based on Spatial Correlation Analysis

Manjeet Singh
Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, India , India

Surender Kumar Soni
Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, India , India

Keywords: clustering, fuzzy logic, localized, wireless sensor network, correlation

Abstract

The wireless sensor networks generally comprise of a large number of sensors. The sensors are disposable and resource-constrained devices. Despite the significant improvement in battery technology, energy conservation is still an imperative function of wireless sensor networks to prolong the network operational lifetime. In the last decade, the clustering approach is normally employed to extend the network operational lifetime, where aggregated sensed information is sent to the base station. The cluster heads are responsible for managing cluster members, information accumulation, and data transmitting. Therefore, the selection of an efficient cluster is a primary concern in the clustered architecture. This paper proposes a correlation model and a localized clustering approach whose goal is to extend the network operational lifetime using fuzzy logic and spatial correlation characteristics. The fuzzy logic is utilized to key out the cluster heads and spatial correlation characteristics are employed to form clusters of closely located sensors in the observing field. Simulation results demonstrate that a significant improvement in energy efficiency can be attained utilizing the proposed approach as compared to the LEACH, CHEF, and DEC approaches.

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References

[1] M. Pietrzak, M. Płaczek, and S. Witczak, “Upward flow of air-oil-water mixture in vertical pipe,” Experimental Thermal and Fluid Science, vol. 81, pp. 175-186, October 2017.

[2] L. M. Al-Hadhrami, S. M. Shaahid, L. O. Tunde, and A. Al-Sarkhi, “Experimental study on the flow regimes and pressure gradients of air-oil-water three-phase flow in horizontal pipes,” Science World Journal, pp. 1-11, 2014.

[3] A. Shmueli, T. E. Unander, and O. J. Nydal, “Characteristics of gas/water/viscous oil in stratified-annular horizontal pipe flows,” in OTC Brasil, October 2015, pp. 1-18.

[4] H. Karami, C. F. Torres, E. Pereyra, and C. Sarica, “Experimental investigation of three-phase low liquid loading flow,” in SPE Annual Technical Conference and Exhibition, September 2015, pp. 28-30.

[5] G. Oddie, H. Shi, L. J. Durlofsky, K. Aziz, B. Pfeffer, and J. A. Holmes, “Experimental study of two and three phase flows in large diameter inclined pipes,” International Journal of Multiphase Flow, vol. 29, no. 4, pp. 527-558, 2003.

[6] M. Descamps, R. V. A. Oliemans, R. F. Mudde, and R. Kusters, “Influence of gas injection on phase inversion in an oil-water flow through a vertical tube,” International Journal of Multiphase Flow, vol. 32, no. 3, pp. 311-322, 2006.

[7] M. N. Descamps, R. V. A. Oliemans, R. F. Mudde, and G. Ooms, “Three-phase gas lift in the laboratory: air bubble injection into oil-water vertical pipe flow,” 13th Interrnational Conference on Multiphase Production and Technology, 2007, pp. 253-261.

[8] H. Shi, J. Holmes, L. Diaz, L. J. Durlofsky, and K. Aziz, “Drift-flux parameters for three-phase steady-state flow in wellbores,” SPE Journal, vol. 10, no. 2, pp. 130-137, 2005.

[9] A. C. Bannwart, O. M. H. Rodriguez, F. E. Trevisan, F. F. Vieira, and C. H. M. de Carvalho, “Experimental investigation on liquid-gas flow: flow patterns and pressure-gradient,” Journal of Petroleum Science and Engineering, vol. 65, no. 1-2, pp. 1-13, March 2009.

[10] P. Poesio, D. Strazza, and G. Sotgia, “Two-and three-phase mixtures of highly-viscous-oil/water/air in a 50 mm i.d. pipe,” Applied Thermal Engineering, vol. 49, pp. 41-47, December 2012.

[11] P. Hanafizadeh, A. Shahani, A. Ghanavati, and M. A. Akhavan-Behabadi, “Experimental investigation of air-water-oil three-phase flow patterns in inclined pipes,” Experimental Thermal and Fluid Science, vol. 84, pp. 286-298, June 2017.

[12] A. R. A. Colmanetti, M. S. de Castro, M. C. Barbosa, and H. M. O. Rodriguez, “Phase inversion phenomena in vertical three-phase flow: experimental study on the influence of fluids viscosity, duct geometry and gas flow rate,” Chemical Engineering Science, vol. 189, pp. 245-259, November 2018.

[13] M. E. Shippen and S. L. Scott, “A neural network model for prediction of liquid holdup in two-phase horizontal flow,” SPE Production Facilities, vol. 19, no. 2, pp. 67-76, 2004.

[14] S. Azizi, E. Ahmadloo, and M. M. Awad, “Prediction of void fraction for gas–liquid flow in horizontal, upward and downward inclined pipes using artificial neural network,” International Journal of Multiphase Flow, vol. 87, pp. 35-44, December 2016.

[15] S. Azizi, M. M. Awad, and E. Ahmadloo, “Prediction of water holdup in vertical and inclined oil-water two-phase flow using artificial neural network,” International Journal of Multiphase Flow, vol. 80, pp. 181-187, April 2016.

[16] J. Ternyik, H. I. Bilgesu, S. Mohaghegh, and D. M. Rose, “Virtual measurement in pipes: part 2-flowing bottom hole pressure under multi-phase flow and inclined wellbore conditions,” in SPE Eastern Regional Meeting, 1995, vol. 1995,

pp. 1-6.

[17] E. A. Osman, “Artificial neural network models for identifying flow regimes and predicting liquid holdup in horizontal multiphase flow,” SPE Production and Facilities, vol. 19, no. 1, pp. 33-40, 2004.

[18] C. C. Tang, S. Tiwari, and A. J. Ghajar, “Effect of void fraction on pressure drop in upward vertical two-phase gas-liquid pipe flow,” Journal of Engineering Gas Turbines and Power, vol. 135, no. 2, pp. 1-7, January 2013.

[19] P. L. Spedding, G. S. Woods, R. S. Raghunathan, and J. K. Watterson, “Vertical two-phase flow,” Chemical Engineering Research Design, vol. 76, pp. 628-634, 1998.

[20] Y. Taitel, D. Bornea, and A. E. Dukler, “Modelling flow pattern transitions for steady upward gas-liquid flow in vertical tubes,” AIChE Journal, vol. 26, no. 3, pp. 345-354, 1980.

[21] A. Skopich, E. Pereyra, C. Sarica, and M. Kelkar, “Pipe-diameter effect on liquid loading in vertical gas wells,” SPE Production and Operations, vol. 30, no. 2, pp. 164-176, 2015.

[22] A. M. Aliyu, Y. D. Baba, L. Lao, H. Yeung, and K. C. Kim, “Interfacial friction in upward annular gas-liquid two-phase flow in pipes,” Experimental Thermal and Fluid Science, vol. 84, pp. 90-109, June 2017.

[23] A. M. Aliyu, A. A. Almabrok, A. Archibong, Y. D. Baba, L. Lao, H. Yeung, and K. C. Kim, “Prediction of entrained droplet fraction in co-current annular gas-liquid flow in vertical pipes,” Experimental Thermal and Fluid Science, vol. 85, pp. 287-304, July 2017.

[24] A. M. Aliyu, A. A. Almabrok, Y. D. Baba, L. Lao, H. Yeung, and K. C. Kim, “Upward gas-liquid two-phase flow after a U-bend in a large-diameter serpentine pipe,” International Journal of Heat and Mass Transfer, vol. 108, pp. 784-800, May 2017.

[25] S. Wongwises and W. Kongkiatwanitch, “Interfacial friction factor in vertical upward gas-liquid annular two-phase flow,” International Commununications in Heat and Mass Transf.er, vol. 28, no. 3, pp. 323-336, 2001.

[26] D. Barnea, “A unified model for predicting flow-pattern transitions for the whole range of pipe inclinations,” International Journal of Multiphase Flow, vol. 13, no. 1, pp. 1-12, 1987.

[27] M. N. Descamps, R. V. A. Oliemans, G. Ooms, and R. F. Mudde, “Experimental investigation of three-phase flow in a vertical pipe: local characteristics of the gas phase for gas-lift conditions,” International Journal of Multiphase Flow, vol. 33, no. 11, pp. 1205-1221, 2007.

[28] F. Al-Ruhaimani, E. Pereyra, C. Sarica, E. M. Al-Safran, and C. F. Torres, “Experimental analysis and model evaluation of high-liquid-viscosity two-phase upward vertical pipe flow,” SPE Journal, vol. 22, no. 03, pp. 712-735, 2017.

[29] J. Dayhoff, “Neural-network architectures: an introduction,” New York: Van Nostrand Reinhold, 1990.

[30] P. Valeh-e-sheyda, F. Yaripour, G. Moradi, and M. Saber, “Application of artificial neural networks for estimation of

the reaction rate in methanol dehydration,” Industrial Engineering and Chemical Research, vol. 49, pp. 4620-4626, 2010.

[31] D. R. Hush and B. G. Horne, “Progress in supervised neural networks,” IEEE Signal Processing Magazine, vol. 10, no. 1, pp. 8-39, 1993.

[32] S. Haykin, “Neural networks,” Convergence, pp. 1-16, 1998.

170 International Journal of Engineering and Technology Innovation, vol. 9, no. 3, 2019, pp. 155 -170

[33] C. Aydiner, I. Demir, and E. Yildiz, “Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal,” Journal of Membrane Science, vol. 248, no. 1-2, pp. 53-62, 2005.

[34] M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989-993, 1994.

[35] L. M. Saini and M. K. Soni, “Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods,” IEE Proceedings - Generation, Transmission and Distribution, vol. 149, no. 5, p. 578, 2002.

[36] Schlumberger, PIPESIM suite, user guide, 2003.

[37] G. S. Woods, P. L. Spedding, J. K. Watterson, and R. S. Raghunathan, “Three-phase oil/water/air vertical flow,” Chemical Engineering Research and Design, vol. 76, no. 5, pp. 571-584, 1998.

[38] S. Guet, O. M. H. Rodriguez, R. V. A. Oliemans, and N. Brauner, “An inverse dispersed multiphase flow model for liquid production rate determination,” International Journal of Multiphase Flow, vol. 32, no. 5, pp. 553-567, 2006.

[39] H. C. Brinkman, “The viscosity of concentrated suspensions and solutions,” Journal of Chemical Physics, vol. 20, no. 4,

pp. 571-571, 1952.

[40] N. Brauner and A. Ullmann, “Modeling of phase inversion phenomenon in two-phase pipe flows,” International Journal of Multiphase Flow, vol. 28, no. 7, pp. 1177-1204, 2002.