Advertisement

Expand Mobile WSN Coverage in Harsh Environments

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

Abstract

Ideally, the lifetime of a homogeneous WSN is maximized when the remaining energy of nodes in the network remains the same. However, most of WSN applications in harsh and complex environments require a kind of nodes heterogeneity, i.e., node mobility; to extend the network coverage and lifetime. In homogeneous WSN, clustering protocols assumed that all the sensor nodes are supplied with the same characteristics, i.e., initial energy. However, placing few heterogeneous nodes in WSN, such as nodes with more computing powers, is an effective way to increase network lifetime and reliability. In this chapter, we propose a sensor clustering method for dynamically organizing heterogeneous WSN using Genetic Algorithm. Moreover, we propose a set of key heterogeneity factors that enhance the performance of WSNs in harsh environments.

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Xie, D., Zhou, Q., You, X., Li, B., & Yuan, X. (2013). A novel energy-efficient cluster formation strategy: From the perspective of cluster members. IEEE Communications Letters, 17(11), 2044–2047.CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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, (pp. 1–22). Springer.  https://doi.org/10.1007/s10586-018-2360-3.
  7. 7.
    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).
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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.  https://doi.org/10.4018/978-1-5225-2229-4.ch045.
  11. 11.
    Heinzelman, W. R., 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
  12. 12.
    Elbhiri, B., Rachid, S., & Elfkihi, S. (2010). Developed distributed energy-effecient clustering (DDEEC) for heterogeneous wireless sensor. In Communications and mobile network, (pp. 1–4). Rabat.Google Scholar
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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.
  18. 18.
    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).
  19. 19.
    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.
  20. 20.
    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.
  21. 21.
    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.
  22. 22.
    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.
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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.CrossRefGoogle Scholar
  26. 26.
    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
  27. 27.
    Iqbal, A., Akbar, M., Javaid, N., Bouk, S., Ilahi, M., & Khan, R. (2013). Advanced LEACH: A static clustering-based heterogeneous routing protocol for WSNs. Journal of Basic and Applied Scientific Research, 3(5), 864–872.Google Scholar
  28. 28.
    Sudeep, T., Kumar, N., & Niu, J. (2014). EEMHR: energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks. International Journal of Communication Systems, 27(9), 1289–1318.CrossRefGoogle Scholar
  29. 29.
    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.
  30. 30.
    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.CrossRefGoogle Scholar
  31. 31.
    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.
  32. 32.
    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.
  33. 33.
    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.
  34. 34.
    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
  35. 35.
    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.
  36. 36.
    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.
  37. 37.
    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.
  38. 38.
    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).
  39. 39.
    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.
  40. 40.
    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 IEEE PES PowerAfrica conference, June 27–30, 2017. Accra-Ghana: IEEE.  https://doi.org/10.1109/PowerAfrica.2017.7991209.
  41. 41.
    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, 2017. Delhi-India: IEEE.Google Scholar
  42. 42.
    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.
  43. 43.
    Kumar, D., Aseri, T., & Patel, R. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor network. Computer Communications, 32(4), 662–667.CrossRefGoogle Scholar
  44. 44.
    Tuah, N., Ismail, M., & Jumari, K. (2011). Energy efficient algorithm for heterogeneous wireless sensor network. In IEEE international conference on control system and computing and engineering, (pp. 92–96). Penang.Google Scholar
  45. 45.
    Javaid, N., Mohammad, N., Latif, K., Qasim, U., Khan, A., & Khan, M. (2013). HEER: hybrid energy efficient reactive protocol for wireless sensor networks. In Saudi international electronics and communications and photonics conference, (pp. 1–4). Riyadh.Google Scholar
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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).
  49. 49.
    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.
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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.
  53. 53.
    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
  54. 54.
    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.CrossRefGoogle Scholar
  55. 55.
    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).
  56. 56.
    Ali, P., Mashhadi, H., & Javadi, S. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114–126.CrossRefGoogle Scholar
  57. 57.
    Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7), 1950–1957.CrossRefGoogle Scholar
  58. 58.
    Bayrakl, S., & Erdogan, S. (2012). Genetic algorithm based energy efficient clusters in wireless sensor networks. Procedia Computer Science, 10, 247–254.CrossRefGoogle Scholar
  59. 59.
    Elhoseny, Mohamed, Elleithy, Khaled, Elminir, Hamdi, Yuan, Xiaohui, & Riad, Alaa. (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
  60. 60.
    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
  61. 61.
    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
  62. 62.
    Hussain, S., Matin, A., & Islam, O. (2007). Genetic algorithm for energy efficient clusters in wireless sensor networks. In The 4th international conference on information technology ITNG, (pp. 147–154). IEEE.Google Scholar
  63. 63.
    Diallo, C., Marot, M., & Becker, M. (2010). Single node cluster reduction in WSN and energy efficiency during cluster formation. In The 9th annual mediterranean ad hoc networking conference, France.Google Scholar
  64. 64.
    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
  65. 65.
    Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.CrossRefGoogle Scholar
  66. 66.
    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
  67. 67.
    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
  68. 68.
    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
  69. 69.
    Karimi, A., Abedini, S., Zarafshan, F., & Al-Haddad, S. (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
  70. 70.
    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
  71. 71.
    Kannammal, K., Purusothaman, T., & Manjusha, M. (2014). An efficient cluster based routing in wireless sensor networks. Journal of Theoretical and Applied Information Technology, 59(3).Google Scholar
  72. 72.
    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
  73. 73.
    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.

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