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Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches

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Abstract

Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime.

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References

  1. Zhao, F., & Guibas, L. J. (2004). Wireless sensor networks: an information processing approach. Burlington: Morgan Kaufmann.

    Google Scholar 

  2. Raghavendra, C. S., Sivalingam, K. M., & Znati, T. (2006). Wireless sensor networks. Berlin: Springer.

    MATH  Google Scholar 

  3. Akyildiz, I. F., & Kasimoglu, I. H. (2004). Wireless sensor and actor networks: research challenges. Ad hoc networks, 2(4), 351–367. Elsevier.

    Article  Google Scholar 

  4. Yao, Y., Cao, Q., Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In 2013 IEEE 10th international conference on mobile ad-hoc and sensor systems (MASS) (pp. 182–190). IEEE.

  5. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98. IEEE.

    Article  Google Scholar 

  6. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 134165.

  7. Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. V. (2016). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, 65(1), 244–255. IEEE.

    Article  MathSciNet  MATH  Google Scholar 

  8. Busch, C., Kannan, R., & Vasilakos, A. V. (2012). Approximating congestion + dilation in networks via” quality of routing games. IEEE Transactions on Computers, 61(9), 1270–1283. IEEE.

    Article  MathSciNet  MATH  Google Scholar 

  9. Dvir, A., & Vasilakos, A. V. (2010). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 40(4), 405–406. ACM.

    Article  Google Scholar 

  10. Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  11. Luo, X., Hu, Y., & Zhu, Y. (2014). Topology evolution model for wireless multi-hop network based on socially inspired mechanism. Physica A: Statistical Mechanics and its Applications, 416, 639–650. Elsevier.

    Article  Google Scholar 

  12. Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557. IEEE.

    Article  Google Scholar 

  13. Hu, X., Li, Y., & Xu, H. (2017). Multi-mode clustering model for hierarchical wireless sensor networks. Physica A: Statistical Mechanics and its Applications, 469, 665–675. Elsevier.

    Article  Google Scholar 

  14. Zhang, X. M., Zhang, Y., Yan, F., & Vasilakos, A. V. (2015). Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754. IEEE.

    Article  Google Scholar 

  15. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113. IEEE.

    Article  Google Scholar 

  16. Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: Literature review and challenges. International Journal of Distributed Sensor Networks, 11, 431047.

  17. Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501. Springer.

    Article  Google Scholar 

  18. Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of Things. Journal of Network and Computer Applications, 42, 120–134. Elsevier.

    Article  Google Scholar 

  19. Vasilakos, A. V., Li, Z., Simon, G., & You, W. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10. Elsevier.

    Article  Google Scholar 

  20. Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1093–1102. IEEE.

    Article  Google Scholar 

  21. Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON) (pp. 46–54). IEEE.

  22. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 23(3), 810–823. IEEE Press.

    Article  Google Scholar 

  23. Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D.-Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554. IEEE.

    Article  Google Scholar 

  24. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802. Elsevier.

    Article  Google Scholar 

  25. Liu, X.-Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197. IEEE.

    Article  Google Scholar 

  26. Zhou, L., Naixue, X., Shu, L., Vasilakos, A., & Yeo, S.-S. (2010). Context-aware middleware for multimedia services in heterogeneous networks. IEEE Intelligent Systems, IEEE.

  27. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45. ACM.

    Article  Google Scholar 

  28. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. IEEE.

    Article  Google Scholar 

  29. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379. IEEE.

    Article  Google Scholar 

  30. Kuila, P., & Jana, P. K. (2014). Energy efficient clusterin and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140. Elsevier.

    Article  Google Scholar 

  31. Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys & Tutorials, 19(1), 550–586. IEEE.

    Article  Google Scholar 

  32. Yoon, K. S. (1981). Multiple attribute decision making. Berlin: Spring.

    MATH  Google Scholar 

  33. Triantaphyllou, E. (2000). Multi-criteria decision making methods: A comparative study (pp. 5–21). Berlin: Springer.

    Book  MATH  Google Scholar 

  34. Yoon, K., & Hwang, C.-L. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer.

    MATH  Google Scholar 

  35. Baker, D., Ephremides, A., & Flynn, J. (1984). The design and simulation of a mobile radio network with distributed control. IEEE Journal on Selected Areas in Communications, 2(1), 226–237. IEEE.

    Article  Google Scholar 

  36. Basagni, S. (1999). Distributed clustering for ad hoc networks. In Proceedings of fourth international symposium on parallel architectures, algorithms, and networks, 1999. (I-SPAN’99), (pp. 310–315). IEEE.

  37. Amis, A. D., Prakash, R., Vuong, T. H. P., & Huynh, D. T. (2000) Max-min d-cluster formation in wireless ad hoc networks. In Proceedings of nineteenth annual joint conference of the IEEE computer and communications societies INFOCOM 2000 1 (pp. 32–41). IEEE.

  38. Demirbas, M., Arora, A., & Mittal, V. (2004). FLOC: A fast local clustering service for wireless sensor networks. Workshop on dependability issues in wireless ad hoc networks and sensor networks (DIWANS/DSN 2004) (pp. 1–6).

  39. Chan, H., & Perrig, A. (2004). ACE: An emergent algorithm for highly uniform cluster formation. Lecture notes in computer science, 2920 (pp. 154–171). Springer.

  40. Ding, P., Holliday, J. A., & Celik, A. (2005). Distributed energy-efficient hierarchical clustering for wireless sensor networks, distributed computing in sensor systems (pp. 466–467). Berlin: Springer.

    Google Scholar 

  41. Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Twenty-second annual joint conference of the IEEE computer and communications INFOCOM 2003. IEEE Societies, 3 (pp. 1713–1723). IEEE.

  42. Ye, M., Li, C., Chen, G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In 24th IEEE international performance, computing, and communications conference IPCCC 2005 (pp. 535–540). IEEE.

  43. Youssef, A., Agrawala, A., Younis, M. (2005). Accurate anchor-free node localization in wireless sensor networks. In 24th IEEE international performance, computing, and communications conference IPCCC 2005 (pp. 465–470). IEEE.

  44. Yuan, H.-Y., Yang, S.-Q., & Yi, Y.-Q. (2011). An energy-efficient unequal clustering method for wireless sensor networks. In 2011 international conference on computer and management (CAMAN) (pp. 1–4). IEEE.

  45. Comeau, F., Sivakumar, S. C., Robertson, W., Phillips, W. J. (2006) Energy conserving architectures and algorithms for wireless sensor networks. In Proceedings of the 39th annual Hawaii international conference on system sciences, 2006. HICSS’06 9 (pp. 236c–236c). IEEE.

  46. Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14), 2842–2852. Elsevier.

    Article  Google Scholar 

  47. Kim, D.-S., & Chung, Y.-J. (2006). Self-organization routing protocol supporting mobile nodes for wireless sensor network. In First international multi-symposiums on computer and computational sciences, 2006, IMSCCS’06 2 (pp. 622–626). IEEE.

  48. Renugadevi, G., & Sumithra, M. G. (2013). An analysis on LEACH-mobile protocol for mobile wireless sensor networks. International Journal of Computer Applications, 65, 21. Foundation of Computer Science.

    Google Scholar 

  49. Kumar, G. S., Vinu, P. M. V., & Jacob, K. P. (2008). Mobility metric based leach-mobile protocol. In 16th International conference on advanced computing and communications, 2008, ADCOM 2008 (pp. 248–253). IEEE.

  50. Corn, J., & Bruce, J. W. (2017). Clustering algorithm for improved network lifetime of mobile wireless sensor networks. 2017 International conference on computing, networking and communications (ICNC) (pp. 1063–1067). IEEE.

  51. Xiangning, F., Yulin, S. (2007). Improvement on LEACH protocol of wireless sensor network. In International conference on sensor technologies and applications, 2007, SensorComm 2007 (pp. 260–264). IEEE.

  52. Yu-quan, Z., & Lei, W. (2010). Improving the LEACH protocol for wireless sensor networks. In 2010, IET.

  53. Li, Y.-Z., Zhang, A.-L., & Liang, Y.-Z. (2013). Improvement of leach protocol for wireless sensor networks. In 2013 Third international conference on instrumentation, measurement, computer, communication and control (IMCCC) (pp. 322–326). IEEE.

  54. Ma, X. W., & Yu, X. (2013). Improvement on LEACH protocol of wireless sensor network. Applied Mechanics and Materials, 347, 1738–1742. Trans Tech Publ.

    Article  Google Scholar 

  55. Koucheryavy, A., Salim, A., & Osamy, W. (2009). Enhanced LEACH protocol for wireless sensor networks. St. Petersburg University of Telecommunication.

  56. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667. Elsevier.

    Article  Google Scholar 

  57. Qureshi, T. N., Javaid, N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). BEENISH: Balanced energy efficient network integrated super heterogeneous protocol for wireless sensor networks. Procedia Computer Science, 19, 920–925. Elsevier.

    Article  Google Scholar 

  58. Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks. Procedia Computer Science, 19, 914–919. Elsevier.

    Article  Google Scholar 

  59. Jin, S., Zhou, M., & Wu, A. S. (2003). Sensor network optimization using a genetic algorithm. In Proceedings of the 7th world multiconference on systemics, cybernetics and informatics (pp. 109–116).

  60. Ferentinos, K. P., Tsiligiridis, T. A., & Arvanitis, K. G. (2005). Energy optimization of wirless sensor networks for environmental measurements. In Proceedings of the international conference on computational intelligence for measurement systems and applicatons (CIMSA) 51 (pp. 1031–1051).

  61. Lee, D., Lee, W., & Kim, J. (2007). Genetic algorithmic topology control for two-tiered wireless sensor networks. Computational Science-ICCS, 2007, 385–392. Springer.

    Google Scholar 

  62. Nitesh, K., Azharuddin, M. & Jana, P. K. (2015). Energy efficient fault-tolerant clustering algorithm for wireless sensor networks. In 2015 International conference on green computing and Internet of Things (ICGCIoT) (pp. 234–239).

  63. Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56. Elsevier.

    Article  Google Scholar 

  64. Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425. Elsevier.

    Article  Google Scholar 

  65. Li, S., Li, L., & Yang, Y. (2011). A local-world heterogeneous model of wireless sensor networks with node and link diversity. Physica A: Statistical Mechanics and Its Applications, 390(6), 1182–1191. Elsevier.

    Article  Google Scholar 

  66. Saaty, T. L. (1990). Decision making for leaders: The analytic hierarchy process for decisions in a complex world. Pittsburgh: RWS Publications.

    Google Scholar 

  67. Saaty, T. L. (2013). Analytic hierarchy process, encyclopedia of operations research and management science (pp. 52–64). Berlin: Springer.

    Book  Google Scholar 

  68. Yaoyao, Y., Juwei, S., Yinong, L., Ping, Z. (2006). Cluster head selection using analytical hierarchy process for wireless sensor networks. 2006 IEEE 17th international symposium on personal, indoor and mobile radio communications (pp. 1–5). IEEE.

  69. Durán, O., & Aguilo, J. (2008). Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications, 34(3), 1787–1794. Elsevier.

    Article  Google Scholar 

  70. Azadeh, A., Ghaderi, S. F., & Izadbakhsh, H. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization. Applied Mathematics and Computation, 195(2), 775–785. Elsevier.

    Article  MathSciNet  MATH  Google Scholar 

  71. Wei, C.-C., Chien, C.-F., & Wang, M.-J. J. (2005). An AHP-based approach to ERP system selection. International Journal of Production Economics, 96(1), 47–62. Elsevier.

    Article  Google Scholar 

  72. Hwang, C.-L., Lai, Y.-J., & Liu, T.-Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899. Elsevier.

    Article  MATH  Google Scholar 

  73. Hamzeloei, F., & Dermany, M. K. (2016). A TOPSIS based cluster head selection for wireless sensor network. Procedia Computer Science, 98, 8–15. Elsevier.

    Article  Google Scholar 

  74. Azad, P., & Sharma, V. (2013). Clusterhead selection using multiple attribute decision making (MADM) approach in wireless sensor networks. International conference on heterogeneous networking for quality, reliability, security and robustness (pp. 141–154). Springer.

  75. Yoon, K. (1987). A reconciliation among discrete compromise solutions. Journal of the Operational Research Society, 38(3), 277–286.

    Article  MATH  Google Scholar 

  76. Assari, A., Mahesh, T. M., & Assari, E. (2012). Role of public participation in sustainability of historical city: Usage of TOPSIS method. Indian Journal of Science and Technology, 5(3), 2289–2294.

    Google Scholar 

  77. Brans, J. P. (1982). The engineering of decision: Elaboration instruments of decision support method PROMETHEE. Laval University, Quebec, Canada, Laval University, Quebec Canada.

  78. Brans, J.-P., & Vincke, P. (1985). Note—A preference ranking organisation method: (The PROMETHEE method for multiple criteria decision-making). Management Science, 31(6), 647–656. INFORMS.

    Article  MathSciNet  MATH  Google Scholar 

  79. Chipcon, A. S. (2004). CC1000: Single chip very low power RF transceiver. 2004-04-20] (2004). http://www.chipcon.com/files/CC1000-Data-Sheet-2-2.pdf. Accessed 20th Apr 2004.

  80. Texas, I. (2001) Instruments, MSP430x13x, MSP430x14x Mixed Signal Microcontroller. Datasheet.

  81. Moteiv corporation. http://www.moteiv.com/xcart/product.php?productid=1. Accessed 2nd Feb 2008.

  82. Crossbow Technology Inc. http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/TelosB_Datasheet.pdf. Accessed 2nd Feb 2008.

  83. Crossbow Technology Inc. http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/6020-0060-01_A_MICAz.pdf. Accessed 2nd Feb 2008.

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    Prince Rajpoot & Pragya Dwivedi

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Rajpoot, P., Dwivedi, P. Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches. Wireless Pers Commun 106, 829–877 (2019). https://doi.org/10.1007/s11277-019-06192-6

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