Skip to main content

Hierarchical and Clustering WSN Models: Their Requirements for Complex Applications

  • Chapter
  • First Online:
Dynamic Wireless Sensor Networks

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

Abstract

Generally, WSN consists of thousands of inexpensive devices, called sensor nodes, capable of computation, communication and sensing events in a specific environment [1,2,3]. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios [4,5,6]. Enormous advances that are emerging in WSNs act as a revolution in all aspects of our life. WSNs have unique specifications describe it and different from other networks. Sensor nodes have energy and computational challenges. Moreover, WSNs may be prone to software failure, unreliable wireless connections, malicious attacks, and hardware faults; that make the network performance may degrade significantly over time. Recently, there is a great interest related to routing process in WSNs using intelligent and machine learning algorithms such as Genetic Algorithms [7,8,9]. Security aspects in routing protocols have not been given enough attention, since most of the routing protocols in WSNs have not been designed with security requirements in mind [10,11,12,13,14]. In this chapter, the main models of WSN with their advantages and limitations are discussed, specially the clustering model. In addition, it provides a literature of the existing clustering methods of WSN that aims to increase the network lifetime. After that, the security aspects are explained in details. Finally, the existing secure clustering methods are discussed and evaluated based on a set of criteria.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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 

  2. 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 

  3. 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.

    Article  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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.

  7. 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.

  8. 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.

    Article  Google Scholar 

  9. 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.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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 

  12. 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 

  13. 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).

  14. 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. https://doi.org/10.1002/sec.1459.

  15. 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.

  16. 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.

  17. 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 

  18. 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.

  19. 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.

  20. 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.

  21. 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 

  22. 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.

  23. 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 

  24. Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement119, 117–128. https://doi.org/10.1016/j.measurement.2018.01.022.

  25. 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).

  26. 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.

  27. 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.

  28. 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.

  29. 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.

  30. 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 

  31. 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 

  32. 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 

  33. 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.

  34. 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.

  35. 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.

  36. 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.

    Article  Google Scholar 

  37. 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 

  38. 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.

  39. 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),

  40. 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)

  41. 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.

  42. 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.

  43. 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 

  44. 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.

  45. Ehsan, S., Bradford, K., Brugger, M., Hamdaoui, B., Kovchegov, Y., Johnson, D., et al. (2012). Design and analysis of delay-tolerant sensor networks for monitoring and tracking free-roaming animals. IEEE Transactions on Wireless Communications, 11(3), 1220–1227.

    Article  Google Scholar 

  46. Stankovic, J., Wood, D., & Tian, H. (2011). Realistic applications for wireless sensor networks in theoretical aspects of distributed computing in sensor networks. Monographs in Theoretical Computer Science, 4, 835–863.

    Google Scholar 

  47. Stankovic, J. (2008). When sensor and actuator networks cover the world. ETRI Journal, 30(5), 627–633.

    Article  Google Scholar 

  48. 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 

  49. Raj, E. (2012). An efficient cluster head selection algorithm for wireless sensor networks EDRLEACH. Journal of Computer Engineering, 2(2), 39–44.

    MathSciNet  Google Scholar 

  50. Ahmed, G., Khan, N., & 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. Ramesh, K., & Somasundaram, K. (2011). A comparative study of clusterhead selection algorithms in wireless sensor networks. International Journal of Computer Science and Engineering Survey, 2(4), 153–164.

    Article  Google Scholar 

  52. 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. https://doi.org/10.1007/s12652-017-0659-1.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Elhoseny .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Elhoseny, M., Hassanien, A.E. (2019). Hierarchical and Clustering WSN Models: Their Requirements for Complex Applications. 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_3

Download citation

Publish with us

Policies and ethics