Advertisement

Extending Homogeneous WSN Lifetime in Dynamic Environments Using the Clustering Model

  • Mohamed ElhosenyEmail author
  • Aboul Ella Hassanien
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 165)

Abstract

To extend the longevity of a homogeneous WSN, the key is to avoid nodes deplete energy before the others. Accordingly, this chapter proposes a new clustering model for WSN used in dynamic environments. In each transmission round, the remaining energy of sensor nodes are fairly even with some fluctuations. That is, as a consequence of the proposed method, the variance among remaining energy is quite low, which implies that the sensor nodes shared the burden of relaying messages and, hence, elongated the overall network life. The main factors that are used in our proposed method for choosing a CH are the distance between the CH and BS, the remaining battery power, and the expected consumed energy.

References

  1. 1.
    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).
  2. 2.
    Wu, Y., & Liu, W. (2013). Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks. IET Wireless Sensor Systems, 3(2), 112–118.CrossRefGoogle Scholar
  3. 3.
    Elhoseny, M., Farouk, A., Zhou, N., Wang, M., Abdalla, S., & Batle, J. (2017a). Dynamic multi-hop clustering in a wireless sensor network: Performance improvement. Wireless Personal Communications, 1–21.Google Scholar
  4. 4.
    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.CrossRefGoogle Scholar
  5. 5.
    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.
  6. 6.
    Elhoseny, M., Tharwat, A., Farouk, A., & Hassanien, A. E. (2017b). K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sensors Letters, 1(4), 1–4.CrossRefGoogle Scholar
  7. 7.
    Elhoseny, M., Tharwat, A., Yuan, X., & Hassanien, A. E. (2018). Optimizing K-coverage of mobile WSNs, Expert Systems with Applications, 92, 142–153.  https://doi.org/10.1016/j.eswa.2017.09.008.CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    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.CrossRefGoogle Scholar
  10. 10.
    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.
  11. 11.
    Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (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).
  12. 12.
    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.
  13. 13.
    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). Cham: Springer.  https://doi.org/10.1007/978-3-319-63754-9_22.
  14. 14.
    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.
  15. 15.
    Abdeldaim, A. M., Sahlol, A. T., Elhoseny, M., & Hassanien, A. E. (2018). Computer-aided acute lymphoblastic Leukemia diagnosis system based on image analysis. In: Hassanien A., & Oliva D. (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.
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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. 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. 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. 22.
    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 & Engineering Research, 6(8), 1243–1252.Google Scholar
  23. 23.
    Yuan, X., Elhoseny, M., El-Minir, H., & Riad, A. (2017). A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. Journal of Network and Systems Management, 25(1), 21–46.CrossRefGoogle Scholar
  24. 24.
    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.
  25. 25.
    Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75–82.  https://doi.org/10.1016/j.eswa.2017.03.021.CrossRefGoogle Scholar
  26. 26.
    Elhoseny, M., Shehab, A., & Yuan, X. (2017). Optimizing robot path in dynamic environments using genetic algorithm and Bezier curve. Journal of Intelligent and Fuzzy Systems, 33(4), 2305–2316. IOS-Press.  https://doi.org/10.3233/JIFS-17348.CrossRefGoogle Scholar
  27. 27.
    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.
  28. 28.
    Elhoseny, M., Farouk, A., Batle, J., Shehab, A., & Hassanien, A. E. (2017). Secure image processing and transmission schema in cluster-based wireless sensor network. In Handbook of research on machine learning innovations and trends, (Chapter 45, pp. 1022–1040), IGI Global, 2017.  https://doi.org/10.4018/978-1-5225-2229-4.ch045.
  29. 29.
    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.
  30. 30.
    Tripathi, K., Singh, N., & Verma, K. (2012). Two-tiered wireless sensor networks–base station optimal positioning case study. IET Wireless Sensor Systems, 2(4), 351–360.CrossRefGoogle Scholar
  31. 31.
    Wang, L., Wang, C., & Liu, C. (2009). Optimal number of clusters in dense wireless sensor networks: A cross-layer approach. IEEE Transactions on Vehicular Technology, 58(2), 966–976.MathSciNetCrossRefGoogle Scholar
  32. 32.
    Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. (2014). Extending self-organizing network availability using genetic algorithm. In International conference on computing, communication and networking technologies (ICCCNT), (pp. 1–6). IEEE.Google Scholar
  33. 33.
    Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H., & Riad, A. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19(12), 2194–2197.CrossRefGoogle Scholar
  34. 34.
    Elhoseny, M., Elminir, H., Riad, A., & Yuan, X. (2016a). 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.CrossRefGoogle Scholar
  35. 35.
    Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. M. (2016b). An energy efficient encryption method for secure dynamic WSN. Security and Communication Networks, 9(13), 2024–2031.Google Scholar
  36. 36.
    Elsayed, W., Elhoseny, M., Riad, A., & Hassanien, A. E. (2017). Autonomic self-healing approach to eliminate hardware faults in wireless sensor networks. In International conference on advanced intelligent systems and informatics, pp. 151–160. Springer.Google Scholar
  37. 37.
    Elsayed, W., Elhoseny, M., Sabbeh, S., & Riad, A. (2017). Self-maintenance model for wireless sensor networks. Computers and Electrical Engineering.  https://doi.org/10.1016/j.compeleceng.2017.12.022. (in Press).
  38. 38.
    Elhoseny, M., Yuan, X., ElMinir, H. K., & Riad, A. M. (2016). An energy efficient encryption method for secure dynamic WSN. Security and Communication Networks, 9(13), 2024–2031.  https://doi.org/10.1002/sec.1459.
  39. 39.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In The Hawaii international conference on system sciences, Maui, Hawaii.Google Scholar
  40. 40.
    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transaction Wireless Communications, 1(4), 660–670.CrossRefGoogle Scholar
  41. 41.
    Chengfa, L., Mao, Y., Guihai, C., & Lie, W. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In IEEE international conference on mobile Ad hoc and sensor systems, Washington, DC.Google Scholar
  42. 42.
    Shirmohammadi, M., Faez, K., & Chhardoli, M. (2009). LELE: Leader election with load balancing energy. In International conference on communications and mobile computing, (pp. 106–110).Google Scholar
  43. 43.
    Raj, E. (2012). An efficient cluster head selection algorithm for wireless sensor networks EDRLEACH. Journal of Computer Engineering, 2(2), 39–44.MathSciNetGoogle Scholar
  44. 44.
    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.CrossRefGoogle Scholar
  45. 45.
    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).
  46. 46.
    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.
  47. 47.
    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.
  48. 48.
    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
  49. 49.
    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.
  50. 50.
    Mahmood, D., Javaid, N., Mahmood, S., Qureshi, S., Memon, A., & 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
  51. 51.
    Lindsey, S., & Raghavendra, C. (2002). Pegasis power-efficient gathering in sensor information systems. IEEE Aerospace Conference Proceedings, 3, 1125–1130.Google Scholar
  52. 52.
    Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor network. In Second international workshop on sensor and actor network protocols and applications.Google Scholar
  53. 53.
    Kashaf, A., Javaid, N., Khan, Z., & Khan, I. (2012). TSEP: Threshold-sensitive stable election protocol for WSNs. In Conference on Frontiers of information technology, (pp. 164–168).Google Scholar
  54. 54.
    Nadeem, Q., Rasheed, M., Javaid1, N., Khan, Z., 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
  55. 55.
    Elbhiri, B., Rachid, S., & Elfkihi, S. (2010). Developed distributed energy-efficient clustering (DDEEC) for heterogeneous wireless sensor. In Communications and mobile network, (pp. 1–4). Rabat.Google Scholar
  56. 56.
    Nandi, B., Barman, S., & Paul, S. (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
  57. 57.
    Bayrakl, S., & Erdogan, S. (2012). Genetic algorithm based energy efficient clusters in wireless sensor networks. Procedia Computer Science, 10, 247–254.CrossRefGoogle Scholar
  58. 58.
    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). DOIurlhttps://doi.org/10.1109/ACCESS.2018.2817615.Google Scholar
  59. 59.
    Rizk-Allah, R. M., Hassanien, A. E., & Elhoseny, M. (2018). Secure and robust fragile watermarking scheme for medical images. IEEE Access, 6(1), 10269–10278.  https://doi.org/10.1109/ACCESS.2018.2799240.CrossRefGoogle Scholar
  60. 60.
    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.
  61. 61.
    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.
  62. 62.
    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.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computers and InformationMansoura UniversityDakahliaEgypt
  2. 2.Department of Information TechnologyCairo UniversityGizaEgypt

Personalised recommendations