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Dynamic Wireless Sensor Networks pp 93–111Cite as

Optimizing Cluster Head Selection in WSN to Prolong Its Existence

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 165))

Abstract

In wireless sensor networks (WNSs), the amount of transferred data is mainly depending on the network lifetime. Hence, the network throughput can be maximized by extending the network lifetime as long as possible. Accordingly, the clustering model is proposed to extend the network lifetime and improve the network performance. However, the optimum network structure in that model may differs from round to round depending on a set of sensor nodes characteristics, i.e, their remaining energy. Getting the intended optimum structure is non trivial process, which includes determining the appropriate number of clusters, electing a cluster head (CH) for each cluster, and assigning each sensor node to a clusters. For that, a new Genetic Algorithm (GA) based model is proposed to form the network structure that optimize its throughput.

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References

  1. Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems. Elsevier. (in Press).

    Google Scholar 

  2. Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117–128. https://doi.org/10.1016/j.measurement.2018.01.022.

    Article  Google Scholar 

  3. Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2017). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing. Springer. https://doi.org/10.1007/s12652-017-0659-1.

  4. Yuan, X., Li, D., Mohapatra, D., & Elhoseny, M. (2017). Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.12.026. (in Press)

  5. Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A.K., Elhoseny, M., & Baik, S.W. (2017). Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems. Elsevier. https://doi.org/10.1016/j.future.2017.11.013.

  6. Shehab A., Elhoseny M., El Aziz M. A., Hassanien A. E. (2018) Efficient schemes for playout latency reduction in P2P-VoD systems. In: A. Hassanien, & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Springer. https://doi.org/10.1007/978-3-319-63754-9_22.

  7. Elhoseny, M., Nabil, A., Hassanien A. E., & Oliva, D. (2018). Hybrid rough neural network model for signature recognition. In A. Hassanien, D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9_14.

  8. Abdeldaim, A. M., Sahlol, A. T., Elhoseny, M., & Hassanien, A. E. (2018). Computer-aided acute lymphoblastic Leukemia diagnosis system based on image analysis. In A. Hassanien, & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9.

  9. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H. K., & Riad, A. M. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19(12), 2194–2197.

    Article  Google Scholar 

  10. Tharwat, A., Mahdi, H., Elhoseny, M., & Hassanien, A. E. (2018). Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Expert Systems With Applications. https://doi.org/10.1016/j.eswa.2018.04.017. Accessed 12 April 2018.

    Article  Google Scholar 

  11. Tharwat, A., Elhoseny, M., Hassanien, A. E., Gabel, T., & Kumar, A. (2018). Intelligent Bezir curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Computing, 1–22. Springer. https://doi.org/10.1007/s10586-018-2360-3.

  12. Sarvaghad-Moghaddam, M., Orouji, A. A., Ramezani, Z., Elhoseny, M., & Farouk, A. (2018). Modelling the Spice parameters of SOI MOSFET using a combinational algorithm. Cluster Computing. Springer. https://doi.org/10.1007/s10586-018-2289-6. (in Press).

  13. Rizk-Allah, R. M., Hassanien, A. E., & Elhoseny, M. (2018). A multi-objective transportation model under neutrosophic environment. Computers and Electrical Engineering. Elsevier. https://doi.org/10.1016/j.compeleceng.2018.02.024. (in Press).

  14. Batle, J., Naseri, M., Ghoranneviss, M., Farouk, A., Alkhambashi, M., & Elhoseny, M. (2017). Shareability of correlations in multiqubit states: Optimization of nonlocal monogamy inequalities. Physical Review A, 95(3), 032123. https://doi.org/10.1103/PhysRevA.95.032123.

  15. Elhoseny, M., Hosny, A., Hassanien, A. E., Muhammad, K., & Sangaiah, A. K. (2017). Secure automated forensic investigation for sustainable critical infrastructures compliant with green computing requirements. IEEE Transactions on Sustainable Computing, PP(99). https://doi.org/10.1109/TSUSC.2017.2782737.

  16. Elhoseny, H., Elhoseny, M., Riad, A. M., & Hassanien, A. E. (2018). A framework for big data analysis in smart cities. In A. Hassanien, M. Tolba, M. Elhoseny, & M. Mostafa (Eds.), AMLTA 2018 the international conference on advanced machine learning technologies and applications (AMLTA2018), Advances in Intelligent Systems and Computing, Vol. 723. Cham: Springer. https://doi.org/10.1007/978-3-319-74690-6_40.

    Chapter  Google Scholar 

  17. Elhoseny, M., Shehab, A., & Osman, L. (2018). An empirical analysis of user behavior for P2P IPTV workloads. In A. Hassanien, M. Tolba, M. Elhoseny, & M. Mostafa (Eds.), AMLTA 2018 the international conference on advanced machine learning technologies and applications (AMLTA2018), Advances in Intelligent Systems and Computing, Vol. 723. Cham: Springer. https://doi.org/10.1007/978-3-319-74690-6_25.

    Chapter  Google Scholar 

  18. Wang, M. M., Qu, Z. G., & Elhoseny, M. (2017). Quantum secret sharing in noisy environment. In X. Sun, H. C. Chao, X. You, & E. Bertino (Eds.), Cloud computing and security, ICCCS 2017. Lecture Notes in Computer Science, Vol. 10603. Cham: Springer.https://doi.org/10.1007/978-3-319-68542-7_9.

    Chapter  Google Scholar 

  19. Elsayed, W., Elhoseny, M., Riad, A. M., & Hassanien, A. E. (2018). Autonomic self-healing approach to eliminate hardware faults in wireless sensor networks. In A. Hassanien, K. Shaalan, T. Gaber, & M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_14.

  20. Abdelaziz, A., Elhoseny, M., Salama, A. S., Riad, A. M., Hassanien, A. E. (2018). Intelligent algorithms for optimal selection of virtual machine in cloud environment, towards enhance healthcare services. In A. Hassanien, K. Shaalan, T. Gaber, M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017, Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_27.

  21. Shehab, A., Ismail, A., Osman, L., Elhoseny, M., & El-Henawy, I. M. (2018). Quantified self using IoT wearable devices. In A. Hassanien, K. Shaalan, T. Gaber, M. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_77.

  22. Elhoseny, M., Elminir, H., Riad, A., & Yuan, X. (2016). A secure data routing schema for WSN using elliptic curve cryptography and homomorphic encryption. Journal of King Saud University–Computer and Information Sciences, 28(3), 262–275.

    Article  Google Scholar 

  23. Tyagia, S., & Kumarb, N. (2013). A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks. Journal of Network and Computer Applications, 36(2), 623–645.

    Article  Google Scholar 

  24. Ali, J., Kumar, G., & Rai, M. K. (2013). Major energy efficient routing schemes in wireless sensor networks. International Journal of Computers and Technology, 4(2), 261–266.

    Google Scholar 

  25. Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. M. (2014). Extending self-organizing network availability using genetic algorithm. In Fifth international conference on computing, communications and networking technologies (ICCCNT), (pp. 1–6).

    Google Scholar 

  26. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. Communications Surveys and Tutorials, 15(2), 551–591.

    Article  Google Scholar 

  27. Du, T., Qu, S., Liu, F., & Wang, Q. (2015). An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Information Fusion.

    Article  Google Scholar 

  28. Riad, A. M., El-Minir, H. K., & Elhoseny, M. (2013). Secure routing in wireless sensor networks a state of the art. International Journal of Computer Applications, 67(7), 7–12.

    Article  Google Scholar 

  29. Yuan, X., Elhoseny, M., El-Minir, H. K., & Riad, A. M. (2017). A genetic algorithm-based dynamic clustering method towards improved WSN longevity. Journal of Network and Systems Management, 25(1), 21–46.

    Article  Google Scholar 

  30. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. Hosseinabadi, A. A. R., Vahidi, J., Saemi, B., Sangaiah, A. K., & Elhoseny, M. (2018). Extended genetic algorithm for solving open-shop scheduling problem. Soft Computing. https://doi.org/10.1007/s00500-018-3177-y.

  33. Metawa, N., Elhoseny, M., Hassan, M. K., & Hassanien, A. E. (2016). Loan portfolio optimization using genetic algorithm: A case of credit constraints. In 2016 12th international computer engineering conference (ICENCO), pp. 59–64.

    Google Scholar 

  34. Elhoseny, M., Tharwat, A., & Hassanien, A. E. (2017c). Bezier curve based path planning in a dynamic field using modified genetic algorithm. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2017.08.004.

  35. Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 7582. https://doi.org/10.1016/j.eswa.2017.03.021.

    Article  Google Scholar 

  36. Elhoseny, M., Shehab, A., & Yuan, X. (2017). Optimizing robot path in dynamic environments using genetic algorithm and Bezier curve. Journal of Intelligent & Fuzzy Systems, 33(4), 2305–2316. IOS-Press. https://doi.org/10.3233/JIFS-17348.

    Article  Google Scholar 

  37. Elhoseny, M., El-Minir, H. K., Riad, A. M., & Yuan, X. (2014). Recent advances of secure clustering protocols in wireless sensor networks. International Journal of Computer Networks and Communications Security, 2(11), 400–413.

    Google Scholar 

  38. Elhoseny, M., Ramírez-González, G., Abu-Elnasr, O. M., Shawkat, S. A., Arunkumar, N., & Farouk, A. (2018). Secure medical data transmission model for IoT-based healthcare systems. IEEE Access, PP(99). https://doi.org/10.1109/ACCESS.2018.2817615.

  39. Shehab, A., Elhoseny, M., Muhammad, K., Sangaiah, A. K., Yang, P., Huang, H., & Hou, G. (2018). Secure and robust fragile watermarking scheme for medical images. IEEE Access, 6(1), 10269–10278. https://doi.org/10.1109/ACCESS.2018.2799240.

    Article  Google Scholar 

  40. Farouk, A., Batle, J., Elhoseny, M., Naseri, M., Lone, M., Fedorov, A., Alkhambashi, M., Ahmed, S.H., Abdel-Aty, M., (2018). Robust general N user authentication scheme in a centralized quantum communication network via generalized GHZ states. Frontiers of Physics, 13, 130306. Springer. https://doi.org/10.1007/s11467-017-0717-3.

  41. Elhoseny, M., Elkhateb, A., Sahlol, A., Hassanien, A. E. (2018). Multimodal biometric personal identification and verification. In A. Hassanien, & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9_12.

  42. Elhoseny, M., Essa, E., Elkhateb, A., Hassanien, A. E., & Hamad, A. (2018). Cascade multimodal biometric system using fingerprint and Iris patterns. In A. Hassanien, K. Shaalan, T. Gaber, & M. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, AISI 2017, Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_55.

  43. Mahmood, D., Javaid, N., Mahmood, S., Qureshi, S., Memon, A. M., & Zaman, T. (2013). Modleach a variant of leach for WSNS. In Eighth international conference on broadband and wireless computing and communication and applications, (pp. 158–163).

    Google Scholar 

  44. Nadeem, Q., Rasheed, M. B., Javaid, N., Khan, Z. A., Maqsood, Y., & Din, A. (2013). M-gear gateway-based energy-aware multi-hop routing protocol for WSNS. In Eighth international conference on broadband and wireless computing and communication and applications, (pp. 164–169).

    Google Scholar 

  45. Li, Q., & Qingxin, Z. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.

    Article  Google Scholar 

  46. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: power-efficient gathering in sensor information systems. In Aerospace conference proceedings, (Vol. 3, pp. 1125–1130).

    Google Scholar 

  47. Kashaf, A., Javaid, N., Khan, Z. A., & Khan, I. A. (2012). TSEP: Threshold-sensitive stable election protocol for WSNS. In Conference on frontiers of information technology, (pp. 164–168).

    Google Scholar 

  48. Elbhiri, B., Saadane, R., & Aboutajdine, D. (2010). Developed distributed energy-efficient clustering (DDEEC) for heterogeneous wireless sensor. In Communications and mobile network (ISVC), (pp. 1–4), Rabat.

    Google Scholar 

  49. Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.

    Article  Google Scholar 

  50. Ahmed, G., Khan, N. M., & Ramer, R. (2008). Cluster head selection using evolutionary computing in wireless sensor networks. In Progress in electromagnetics research symposium, (pp. 883–886).

    Google Scholar 

  51. Bhaskar, N., Subhabrata, B., & Soumen, P. (2010). Genetic algorithm based optimization of clustering in ad-hoc networks. International Journal of Computer Science and Information Security, 7(1), 165–169.

    Google Scholar 

  52. Asim, M., & Mathur, V. (2013). Genetic algorithm based dynamic approach for routing protocols in mobile ad hoc networks. Journal of Academia and Industrial Research, 2(7), 437–441.

    Google Scholar 

  53. Karimi, A., Abedini, S. M., Zarafshan, F., & Al-Haddad, S. A. R. (2013). Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network. Journal of Basic and Applied Scientific Research, 3(4), 694–703.

    Google Scholar 

  54. Rana, K., & Zaveri, M. (2013). Synthesized cluster head selection and routing for two tier wireless sensor network. Journal of Computer Networks and Communications, 13(3).

    Google Scholar 

  55. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for energy efficient clusters in wireless sensor networks. In International conference on information technology.

    Google Scholar 

  56. Sivagami, A., M. Rathnakumar. (2013). Economic generation scheduling using genetic algorithm. Social Science Research Network.

    Google Scholar 

  57. El Aziz, M. A., Hemdan, A. M., Ewees, A. A., Elhoseny, M., Shehab, A., Hassanien, A. E., & Xiong, S. (2017). Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In 2017 IEEE PES PowerAfrica Conference, (pp. 115–120), June 27–30, 2017. Accra-Ghana: IEEE. https://doi.org/10.1109/PowerAfrica.2017.7991209.

  58. Ewees, A. A., El Aziz, M. A., & Elhoseny, M. (2017). Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In 8th international conference on computing, communication and networking technologies (8ICCCNT), July 3–5. Delhi-India: IEEE.

    Google Scholar 

  59. Metawa, N., Elhoseny, M., Hassan, M. K., & Hassanien, A. E. (2016) Loan portfolio optimization using Genetic Algorithm: A case of credit constraints. In Proceedings of 12th international computer engineering conference (ICENCO), (pp. 59–64). IEEE. https://doi.org/10.1109/ICENCO.2016.7856446.

  60. Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. M. (2016). An energy efficient encryption method for secure dynamic WSN. Security and Communication Networks, 9(13), 2024–2031.

    Google Scholar 

  61. Elhoseny, M., Elleithy, K., Elminir, H., Yuan, X., & Riad, A. (2015). Dynamic clustering of heterogeneous wireless sensor networks using a genetic algorithm towards balancing energy exhaustion. International Journal of Scientific and Engineering Research, 6(8), 1243–1252.

    Google Scholar 

  62. Ahmed, G., Khan, N. M., Khalid, Z., & Ramer, R. (2008). Cluster head selection using decision trees for wireless sensor networks. In Sensor networks and information processing, (pp. 173–178).

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computers and Information, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  2. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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  1. Mohamed Elhoseny
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  2. Aboul Ella Hassanien
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Correspondence to Mohamed Elhoseny .

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Elhoseny, M., Hassanien, A.E. (2019). Optimizing Cluster Head Selection in WSN to Prolong Its Existence. In: Dynamic Wireless Sensor Networks. Studies in Systems, Decision and Control, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-92807-4_5

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