Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network | Wireless Personal Communications Skip to main content

Advertisement

Log in

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

  • Research
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Efficiency in energy consumption remains a critical concern in Wireless Sensor Networks due to their reliance on battery power. The major cause of this problem is the way Cluster Heads are chosen in clustering methods. This paper describes a custom- made Enhanced Energy Efficient Clustering and Routing protocol that can be used to solve these problems. The protocol has three steps which include; initially, application of k-means clustering technique to cluster nodes and select best CH nodes from each group, secondly designation of Super Cluster Head using Adaptive Neuro Fuzzy Inference System among CHs, and finally, determination of the most energy efficient multi path routing strategy for data transfer by utilizing Black Widow Optimization algorithm. Simulations results show that the proposed scheme outperforms other alternatives in terms of energy consumption, end-to-end latency and throughput. When compared with existing approaches, this method achieves a peak data rate of 23007 kbps with minimum energy consumption at 6 J as well as reduced delay time at 7.0533 ms.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Zachariah, U. E., & Kuppusamy, L. (2022). A hybrid approach to energy efficient clustering and routing in wireless sensor networks. Evolutionary Intelligence, 15(1), 593–605.

    Article  Google Scholar 

  2. Selvi, M., Santhosh Kumar, S. V. N., Ganapathy, S., Ayyanar, A., Nehemiah, H. K., & Kannan, A. (2021). An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Personal Communications, 116(1), 61–90.

    Article  Google Scholar 

  3. Preeth, S. K., Dhanalakshmi, R., & Mohamed Shakeel, P. (2020). An intelligent approach for energy efficient trajectory design for mobile sink based IoT supported wireless sensor networks. Peer-to-Peer networking and applications, 13(6), 2011–2022.

    Article  Google Scholar 

  4. Khan, A., Khan, M., Ahmed, S., Rahman, M. A. A., & Khan, M. (2019). Energy harvesting based routing protocol for underwater sensor networks. PLoS ONE, 14(7), e0219459.

    Article  Google Scholar 

  5. Thangaramya, K., Kanagasabai Kulothungan, R., Logambigai, M. S., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223. https://doi.org/10.1016/j.comnet.2019.01.024

    Article  Google Scholar 

  6. Singh, S., & Saini, H. S. (2022). Intelligent ad-hoc-on demand multipath distance vector for wormhole attack in clustered WSN. Wireless Personal Communications, 122(2), 1305–1327. https://doi.org/10.1007/s11277-021-08950-x

    Article  Google Scholar 

  7. Baniata, M., Reda, H. T., Chilamkurti, N., & Abuadbba, A. (2021). Energy-efficient hybrid routing protocol for IoT communication systems in 5G and beyond. Sensors, 21(2), 537. https://doi.org/10.3390/s21020537

    Article  Google Scholar 

  8. Arjunan, S., & Pothula, S. (2019). A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 31(3), 304–317.

    Article  Google Scholar 

  9. Govender, P., & Sivakumar, V. (2020). Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research, 11(1), 40–56.

    Article  Google Scholar 

  10. Dhiman, G., & Kaur, A. (2019). A Hybrid Algorithm Based on Particle Swarm and Spotted Hyena Optimizer for Global Optimization. In Jagdish Chand Bansal, Kedar Nath Das, Atulya Nagar, Kusum Deep, & Akshay Kumar Ojha (Eds.), Soft Computing for Problem Solving (pp. 599–615). Singapore: Springer. https://doi.org/10.1007/978-981-13-1592-3_47

    Chapter  Google Scholar 

  11. Shi, B., & Zhang, Y. (2021). A novel algorithm to optimize the energy consumption using IoT and based on Ant Colony Algorithm. Energies, 14(6), 1709.

    Article  Google Scholar 

  12. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 102317.

    Article  Google Scholar 

  13. Hosseini, S. M., Joloudari, J. H., & Saadatfar, H. (2019). MB-FLEACH: a new algorithm for super cluster head selection for wireless sensor networks. International Journal of Wireless Information Networks., 26(2), 113–130. https://doi.org/10.1007/s10776-019-00427-w

    Article  Google Scholar 

  14. Kavidha, V., & Ananthakumaran, S. (2019). Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink. Peer-to-Peer Networking and Applications, 12(4), 881–892.

    Article  Google Scholar 

  15. Agarkhed, J., Kadrolli, V., & Patil, S. (2020). Fuzzy based multi-level multi-constraint multi-path reliable routing in wireless sensor network. International Journal of Information Technology, 12(4), 1133–1146.

    Article  Google Scholar 

  16. Ezhilarasi, M., & Krishnaveni, V. (2019). An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks. Soft Computing, 23(18), 8367–8377.

    Article  Google Scholar 

  17. Balaji, S., Golden Julie, E., & Harold Robinson, Y. (2019). Development of fuzzy based energy efficient cluster routing protocol to increase the lifetime of wireless sensor networks. Mobile Networks and Applications, 24(2), 394–406. https://doi.org/10.1007/s11036-017-0913-y

    Article  Google Scholar 

  18. Moussa, N., Hamidi-Alaoui, Z., El Belrhiti, A., & Alaoui, El. (2020). ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks. Wireless Networks, 26(4), 2915–2928. https://doi.org/10.1007/s11276-019-02247-5

    Article  Google Scholar 

  19. Bhattacharjya, K., Alam, S., & De, D. (2019). CUWSN: energy efficient routing protocol selection for cluster based underwater wireless sensor network. Microsystem Technologies., 28(2), 543–559. https://doi.org/10.1007/s00542-019-04583-0

    Article  Google Scholar 

  20. Mehta, D., & Saxena, S. (2020). MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustainable Computing: Informatics and Systems, 28, 100406.

    Google Scholar 

  21. Shagari, N. M., Idris, M. Y. I., Salleh, R. B., Ahmedy, I., Murtaza, G., & Shehadeh, H. A. (2020). Heterogeneous energy and traffic aware sleep-awake cluster-based routing protocol for wireless sensor network. IEEE Access, 8, 12232–12252. https://doi.org/10.1109/ACCESS.2020.2965206

    Article  Google Scholar 

  22. Hosseinzadeh, M., Ionescu-Feleaga, L., Ionescu, B. -Ș, Sadrishojaei, M., Kazemian, F., Rahmani, A. M., & Khan, F. (2022). A hybrid delay aware clustered routing approach using aquila optimizer and firefly algorithm in internet of things. Mathematics, 10(22), 4331. https://doi.org/10.3390/math10224331

    Article  Google Scholar 

  23. Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2023). An energy-aware scheme for solving the routing problem in the internet of things based on jaya and flower pollination algorithms. Journal of Ambient Intelligence and Humanized Computing., 14(8), 11363–11372. https://doi.org/10.1007/s12652-023-04650-5

    Article  Google Scholar 

  24. Sadrishojaei, M., & Kazemian, F. (2023). Development of an enhanced blockchain mechanism for internet of things authentication. Wireless Personal Communications, 132(4), 2543–2561.

    Article  Google Scholar 

  25. Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2022). An energy-aware IoT routing approach based on a swarm optimization algorithm and a clustering technique. Wireless Personal Communications, 127(4), 3449–3465.

    Article  Google Scholar 

  26. Hatamian, M., Barati, H., Movaghar, A., & Naghizadeh, A. (2016). CGC: Centralized genetic-based clustering protocol for wireless sensor networks using onion approach. Telecommunication systems, 62, 657–674.

    Article  Google Scholar 

  27. Nezhad, A., Maryam, H. B., & Barati, A. (2022). An authentication-based secure data aggregation method in internet of things. Journal of Grid Computing., 20(3), 29.

    Article  Google Scholar 

  28. Hatamian, M., Bardmily, M. A. L. M. A. S. I., Asadboland, M., Hatamian, M., & Barati, H. (2016). Congestion-aware routing and fuzzy-based rate controller for wireless sensor networks. Radioengineering., 25(1), 114–123.

    Article  Google Scholar 

  29. Elham, G. D., & Barati, Hamid. (2023). Cluster based routing method using mobile sinks in wireless sensor network. International Journal of Electronics., 110(2), 360–372.

    Article  Google Scholar 

  30. Kiamansouri, E., Barati, H., & Barati, A. (2022). A two-level clustering based on fuzzy logic and content-based routing method in the internet of things. Peer-to-Peer Networking and Applications, 15(4), 2142–2159.

    Article  Google Scholar 

  31. Akbari, M. R., Barati, H., & Barati, A. (2022). An efficient gray system theory-based routing protocol for energy consumption management in the Internet of Things using fog and cloud computing. Computing., 104(6), 1307–1335.

    Article  Google Scholar 

  32. Akbari, M. R., Barati, H., & Barati, A. (2022). An overlapping routing approach for sending data from things to the cloud inspired by fog technology in the large-scale IoT ecosystem. Wireless Networks., 28(2), 521–538.

    Article  Google Scholar 

  33. Patidar, Y., Jain, M., & Vyas, A. K. (2024). Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network. SN Computer Science., 5(2), 218.

    Article  Google Scholar 

  34. Panchal, A., & Singh, R. K. (2021). Eadcr: energy aware distance based cluster head selection and routing protocol for wireless sensor networks. Journal of Circuits, Systems and Computers, 30(04), 2150063.

    Article  Google Scholar 

  35. Panchal, A., & Singh, R. K. (2021). FFTHR: Fitness Function based Two-Hop Routing in WSN. Internet Technology Letters, 4(4), e217.

    Article  Google Scholar 

  36. Feng, Z.-K., Niu, W.-J., Zhang, R., Wang, S., & Cheng, C.-T. (2019). Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. Journal of hydrology, 576, 229–238.

    Article  Google Scholar 

  37. Park, Geon Yong, Heeseong Kim, Hwi Woon Jeong, and Hee Yong Youn. "A novel cluster head selection method based on K-means algorithm for energy efficient wireless sensor network." In 2013 27th international conference on advanced information networking and applications workshops, pp. 910–915. IEEE, 2013.

  38. Kuncheva, L. I., & Steimann, F. (1999). Fuzzy diagnosis. Artificial intelligence in medicine, 16(2), 121–128.

    Article  Google Scholar 

  39. Jang, J.-S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics., 23(3), 665–685.

    Article  Google Scholar 

  40. Kar, S., Pandit, A. R., & Biswal, K. C. (2020). Prediction of FRP shear contribution for wrapped shear deficient RC beams using adaptive neuro-fuzzy inference system (ANFIS). Structures, 23, 702–717. https://doi.org/10.1016/j.istruc.2019.10.022

    Article  Google Scholar 

  41. Ewees, Ahmed A., Mohamed Abd El Aziz, and Mohamed Elhoseny. "Social-spider optimization algorithm for improving ANFIS to predict biochar yield." In 2017 8th international conference on computing, communication and networking technologies (ICCCNT), pp. 1–6. IEEE, 2017.

  42. Pattnaik, S., & Sahu, P. K. (2021). Adaptive Neuro-Fuzzy Inference System-Particle swarm optimization-based clustering approach and hybrid Moth-flame cuttlefish optimization algorithm for efficient routing in wireless sensor network. International Journal of Communication Systems., 34(9), e4783.

    Article  Google Scholar 

  43. Sun, Y., Dong, W., & Chen, Y. (2017). An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE communications Letters, 21(6), 1317–1320.

    Article  Google Scholar 

  44. Wang, Z., Ding, H., Li, Bo., Bao, L., & Yang, Z. (2020). An energy efficient routing protocol based on improved artificial bee colony algorithm for wireless sensor networks. IEEE Access, 8, 133577–133596.

    Article  Google Scholar 

  45. Hayyolalam, V., & Kazem, A. A. P. (2020). Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249.

    Article  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, Design and Implementation done by M.R.S and I.S.A. Data analysis and Verification of algorithm done by R.N and S.M.H. All authors reviewed the manuscript.

Corresponding author

Correspondence to M. R. Senkumar.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senkumar, M.R., Arafat, I.S., Nathiya, R. et al. Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network. Wireless Pers Commun 138, 1531–1558 (2024). https://doi.org/10.1007/s11277-024-11549-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11549-7

Keywords