International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Energy-Aware Optimal Clustering and Secure Routing Protocol for Heterogeneous Wireless Sensor Network

Author NameAuthor Details

Swapna M P, G. Satyavathy

Swapna M P[1]

G. Satyavathy[2]

[1]Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, Tamil Nadu, India

[2]Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, Tamil Nadu, India

Cite this Article

DOI: 10.22247/ijcna/2022/211594

Pages : 12-21

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Abstract

Wireless Sensor Network (WSN) is a collection of low energy sensor nodes deployed in hostile complex environments. Their functionality gathers requisite data from the environment and transmits it to the base station for further processing. To enhance the performance of WSN, sensor nodes with different energy levels, capabilities and functionalities are deployed, leading to Heterogeneous WSN (HWSN). The initial energy, energy consumption rate, and residual energy differ for each node in a heterogeneous WSN. Many algorithms were proposed to accomplish an energy-efficient steady HWSN, but the performance level is not satisfactory. This paper presents a novel integrated approach, Energy-Aware Optimal Clustering & Securing Routing (EAOCSR). The algorithm amalgamated three techniques optimal clustering, reliable routing and secured transmission, considering energy retention and network lifetime as the vital parameters. Unequal clustering scheme, trust-based reliable and secure routing forms the core of EAOCSR. The performance of EAOCSR is analyzed using MATLAB simulations. It reveals that the proposed routing protocol EAOCSR has superior performance to existing protocols regarding energy utilization, throughput, network lifetime, stability and security.

Index Terms

HWSN

Unequal Clustering

Trust

Blockchain

Stability

Security

Reference

  1. 1.
    S. De and B. Chakraborty, “An energy-efficient wireless sensor network construction algorithm for air quality condition detection system,” Comput. Electr. Eng., vol. 91, p. 107064, 2021, doi: https://doi.org/10.1016/j.compeleceng.2021.107064.
  2. 2.
    A. I. Saleh, K. M. Abo-Al-Ez, and A. A. Abdullah, “A Multi-Aware Query Driven (MAQD) routing protocol for mobile wireless sensor networks based on neuro-fuzzy inference,” J. Netw. Comput. Appl., vol. 88, pp. 72–98, 2017, doi: https://doi.org/10.1016/j.jnca.2017.02.016.
  3. 3.
    M. Farsi, M. Badawy, M. Moustafa, H. A. Ali, and Y. Abdulazeem, “A Congestion-Aware Clustering and Routing (CCR) Protocol for Mitigating Congestion in WSN,” IEEE Access, vol. 7, pp. 105402–105419, 2019, doi: 10.1109/ACCESS.2019.2932951.
  4. 4.
    Z. Zhou, B. Yao, R. Xing, L. Shu, and S. Bu, “E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things,” IEEE Sens. J., vol. 16, no. 11, pp. 4072–4082, 2016, doi: 10.1109/JSEN.2015.2437904.
  5. 5.
    K. Kalaivanan and V. Bhanumathi, “Reliable location aware and Cluster-Tap Root based data collection protocol for large scale wireless sensor networks,” J. Netw. Comput. Appl., vol. 118, pp. 83–101, 2018, doi: https://doi.org/10.1016/j.jnca.2018.06.005.
  6. 6.
    J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
  7. 7.
    J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
  8. 8.
    J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
  9. 9.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  10. 10.
    J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
  11. 11.
    J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.
  12. 12.
    R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.
  13. 13.
    D. Sharma and A. P. Bhondekar, “Traffic and Energy Aware Routing for Heterogeneous Wireless Sensor Networks,” IEEE Commun. Lett., vol. 22, no. 8, pp. 1608–1611, Aug. 2018, doi: 10.1109/LCOMM.2018.2841911.
  14. 14.
    S. Al-Sodairi and R. Ouni, “Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks,” Sustain. Comput. Informatics Syst., vol. 20, pp. 1–13, Dec. 2018, doi: 10.1016/j.suscom.2018.08.007.
  15. 15.
    A. Benzerbadj, B. Kechar, A. Bounceur, and M. Hammoudeh, “Surveillance of sensitive fenced areas using duty-cycled wireless sensor networks with asymmetrical links,” J. Netw. Comput. Appl., vol. 112, pp. 41–52, 2018, doi: https://doi.org/10.1016/j.jnca.2018.03.027.
  16. 16.
    R. Yarinezhad and S. N. Hashemi, “Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure,” Pervasive Mob. Comput., vol. 58, p. 101033, 2019, doi: https://doi.org/10.1016/j.pmcj.2019.101033.
  17. 17.
    S. Chinnaswamy and A. K, “Trust aggregation authentication protocol using machine learning for IoT wireless sensor networks,” Comput. Electr. Eng., vol. 91, p. 107130, 2021, doi: https://doi.org/10.1016/j.compeleceng.2021.107130.
  18. 18.
    B. Shah et al., “Guaranteed lifetime protocol for IoT based wireless sensor networks with multiple constraints,” Ad Hoc Networks, vol. 104, p. 102158, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102158.
  19. 19.
    A. Chowdhury and D. De, “FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks,” Comput. Commun., vol. 163, pp. 12–34, 2020, doi: https://doi.org/10.1016/j.comcom.2020.09.002.
  20. 20.
    G. D. O’Mahony, K. G. McCarthy, P. J. Harris, and C. C. Murphy, “Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices,” Ad Hoc Networks, vol. 120, p. 102562, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102562.
  21. 21.
    M. Faheem, M. A. Ngadi, and V. C. Gungor, “Energy efficient multi-objective evolutionary routing scheme for reliable data gathering in Internet of underwater acoustic sensor networks,” Ad Hoc Networks, vol. 93, p. 101912, 2019, doi: https://doi.org/10.1016/j.adhoc.2019.101912.
  22. 22.
    J. Lu, L. Feng, J. Yang, M. M. Hassan, A. Alelaiwi, and I. Humar, “Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks,” Futur. Gener. Comput. Syst., vol. 95, pp. 45–51, 2019, doi: https://doi.org/10.1016/j.future.2018.12.024.
  23. 23.
    S. Chouikhi, I. El Korbi, Y. Ghamri-Doudane, and L. Azouz Saidane, “Distributed connectivity restoration in multichannel wireless sensor networks,” Comput. Networks, vol. 127, pp. 282–295, 2017, doi: https://doi.org/10.1016/j.comnet.2017.08.016.
  24. 24.
    S. Priyadarshani, A. Tomar, and P. K. Jana, “An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks,” Ad Hoc Networks, vol. 113, p. 102407, 2021, doi: https://doi.org/10.1016/j.adhoc.2020.102407.
  25. 25.
    P. S. Uma Priyadarsini and P. Sriramya, “Disaster management using evidence-based interactive trust management system for wireless sensor networks by Internet of Things,” Comput. Electr. Eng., vol. 75, pp. 164–174, 2019, doi: https://doi.org/10.1016/j.compeleceng.2019.02.020.
  26. 26.
    O. A. Khashan, R. Ahmad, and N. M. Khafajah, “An automated lightweight encryption scheme for secure and energy-efficient communication in wireless sensor networks,” Ad Hoc Networks, vol. 115, p. 102448, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102448.
  27. 27.
    C. Luo, J. Yu, D. Li, H. Chen, Y. Hong, and L. Ni, “A Novel Distributed algorithm for constructing virtual backbones in wireless sensor networks,” Comput. Networks, vol. 146, pp. 104–114, 2018, doi: https://doi.org/10.1016/j.comnet.2018.09.016.
  28. 28.
    N. Kaur and S. Singh, “Optimized cost effective and energy efficient routing protocol for wireless body area networks,” Ad Hoc Networks, vol. 61, pp. 65–84, 2017, doi: https://doi.org/10.1016/j.adhoc.2017.03.008.
  29. 29.
    G. J., S. J., S. Y., and M. V., “Data consistency matrix based data processing model for efficient data storage in wireless sensor networks,” Comput. Commun., vol. 151, pp. 172–182, 2020, doi: https://doi.org/10.1016/j.comcom.2019.12.060.
  30. 30.
    M. M. Ahmed, E. H. Houssein, A. E. Hassanien, A. Taha, and E. Hassanien, “Maximizing lifetime of wireless sensor networks based on whale optimization algorithm,” Adv. Intell. Syst. Comput., vol. 639, pp. 724–733, 2018, doi: 10.1007/978-3-319-64861-3_68.
  31. 31.
    J. Sengupta, S. Ruj, and S. Das Bit, “End to end secure anonymous communication for secure directed diffusion in IoT,” PervasiveHealth Pervasive Comput. Technol. Healthc., pp. 445–450, Jan. 2019, doi: 10.1145/3288599.3295577.
  32. 32.
    X. Yu, F. Li, T. Li, N. Wu, H. Wang, and H. Zhou, “Trust-based secure directed diffusion routing protocol in WSN,” J. Ambient Intell. Humaniz. Comput. 2020, pp. 1–13, Nov. 2020, doi: 10.1007/S12652-020-02638-Z.
  33. 33.
    S. M. Pournaghi, M. Bayat, and Y. Farjami, “MedSBA: a novel and secure scheme to share medical data based on blockchain technology and attribute-based encryption,” J. Ambient Intell. Humaniz. Comput. 2020 1111, vol. 11, no. 11, pp. 4613–4641, Jan. 2020, doi: 10.1007/S12652-020-01710-Y.
  34. 34.
    M. Hema Kumar, V. Mohanraj, Y. Suresh, J. Senthilkumar, and G. Nagalalli, “Trust aware localized routing and class based dynamic block chain encryption scheme for improved security in WSN,” J. Ambient Intell. Humaniz. Comput. 2020 125, vol. 12, no. 5, pp. 5287–5295, Apr. 2020, doi: 10.1007/S12652-020-02007-W.
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