Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

An Energy Efficient Clustered Gravitational and Fuzzy Based Routing Algorithm in WSNs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks consist of many tiny sensor nodes which are deployed in various geographical locations for sensing the normal spectacles and also to transmit the collected information to the base station which is also named destination node through multiple nodes present in the network. Most of the existing heuristics algorithms used for finding the optimal routes have limitations in the provision of effective solutions for routing and clustering mechanisms in larger search spaces. Hence, when the search space increases exponentially, the chance of creating the optimal solution for clustering and routing is decreasing and ultimately an un-optimized process depletes the sensor node resources. In order to address the challenges and limitations present in the existing routing systems, two new heuristics algorithms namely gravitational approach based clustering method and a clustered gravitational routing algorithm have been proposed in this paper for providing an optimal solution for efficient clustering and effective routing. Moreover, a fuzzy logic based deductive inference system has been designed and used in this work for selecting the most appropriate nodes as cluster head nodes from the nodes present in each cluster. The simulation results obtained from this work show that the clustering accuracy and the network lifetime are increased and the energy consumption as well as delay are reduced with the application of these proposed algorithms.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Shafiq, M., Ashraf, H., Ullah, A., & Tahira, S. (2020). Systematic literature review on energy efficient routing schemes in WSN—A survey. Mobile Network Applications, 25, 882–895.

    Google Scholar 

  2. He, W. (2019). Energy-saving algorithm and simulation of wireless sensor networks based on clustering routing protocol. IEEE Access, 7, 172505–172514.

    Google Scholar 

  3. Shivappa, N., & Manvi, S. S. (2019). Fuzzy-based cluster head selection and cluster formation in wireless sensor networks. IET Networks, 8(6), 390–397.

    Google Scholar 

  4. El Alami, H., & Najid, A. (2019). ECH: An enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access, 7, 107142–107153.

    Google Scholar 

  5. Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Khannah Nehemiah, H., & Kannan, A. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications, 105(4), 1475–1490.

    Google Scholar 

  6. Priya, S., Tamizharasan, P. S., & Kannan, A. (2019). Fuzzy genetic elliptic curve Diffie Hellman algorithm for secured communication in networks. Wireless Personal Communications, 105(3), 993–1007.

    Google Scholar 

  7. Ogundile, O. O., Balogun, M. B., Ijiga, O. E., & Falayi, E. O. (2019). Energy-balanced and energy-efficient clustering routing protocol for wireless sensor networks. IET Communications, 13(10), 1449–1457.

    Google Scholar 

  8. Nancy, P., Muthurajkumar, S., Ganapathy, S., Santhosh Kumar, S. V. N., Selvi, M., & Kannan, A. (2020). Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Communications, 14(5), 888–895.

    Google Scholar 

  9. Beheshtiasl, A., & Ghafari, A. (2019). Secure and trust-aware routing scheme in wireless sensor networks. Wireless Personal Communications, 107, 1799–1814.

    Google Scholar 

  10. Jain, A., & Ashok Kumar, G. (2020). Energy efficient fuzzy routing protocol for wireless sensor networks. Wireless Personal Communications, 110, 1459–1474.

    Google Scholar 

  11. Mazinani, A., Mazinani, S. M., & Mirzaie, M. (2019). FMCR-CT: An energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network. Alexandria Engineering Journal, 58(1), 127–141.

    Google Scholar 

  12. Logambigai, R., & Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 22, 945–957.

    Google Scholar 

  13. Kundu, S. (1999). Gravitational clustering: A new approach based on the spatial distribution of the points. Journal of Pattern Recognition, 32, 1149–1160.

    Google Scholar 

  14. Selvi, M., Logambigai, R., Ganapathy, S., Sai Ramesh, L., Khanna Nehemiah, H., & Kannan, A. (2006). Fuzzy temporal approach for energy efficient routing in WSN. In Proceedings of the international conference on informatics and analytics (pp. 1–5). ACM.

  15. Bitam, S., Mellouk, A., & Zeadally, S. (2015). Bio-inspired routing algorithms survey for vehicular ad hoc networks. IEEE Communication Surveys and Tutorials, 17(2), 843–867.

    Google Scholar 

  16. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Google Scholar 

  17. Chi, Y. P., & Chang, H. P. (2013). An energy-aware grid-based routing scheme for wireless sensor networks. Telecommunication Systems, 54(4), 403–415.

    Google Scholar 

  18. Selvi, M., Velvizhy, P., Ganapathy, S., Khanna-Nehemiah, H., & Kannan, A. (2019). A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Computing, 22(5), 10839–10848.

    Google Scholar 

  19. Selvi, M., Logambigai, R., Ganapathy, S., Khanna Nehemiah, H., & Kannan, A. (2017). An intelligent agent and FSO based efficient routing algorithm for wireless sensor network. In Proceedings of the second international conference on recent trends and challenges in computational models (ICRTCCM) (pp. 100–105). IEEE.

  20. Logambigai, R., Ganapathy, S., & Kannan, A. (2018). Energy-efficient grid-based routing algorithm using intelligent fuzzy rules for wireless sensor networks. Computers & Electrical Engineering, 68, 62–75.

    Google Scholar 

  21. Kalidoss, T., Rajasekaran, L., Kanagasabai, K., Ganapathy, S., & Kannan, A. (2020). QoS aware trust based routing algorithm for wireless sensor networks. Wireless Personal Communications, 110(4), 1637–1658.

    Google Scholar 

  22. Hua, E. Y., & Haas, Z. J. (2015). Mobile-projected trajectory algorithm with velocity-change detection for predicting residual link lifetime in MANET. IEEE Transactions on Vehicular Technology, 64(3), 1065–1078.

    Google Scholar 

  23. Tsai, C.-W., Hong, T.-P., & Shiu, G.-N. (2016). Metaheuristics for the lifetime of WSN: A review. IEEE Sensors Journal, 16(9), 2812–2831.

    Google Scholar 

  24. Zahedi, Z. M., Akbari, R., Shokouhifar, M., Safaei, F., & Jalali, A. (2016). Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Systems with Applications, 55, 313–328.

    Google Scholar 

  25. Chen, Y., & Yang, H. (2016). Sparse modeling and recursive prediction of space–time dynamics in stochastic sensor networks. IEEE Transactions on Automation Science and Engineering, 13(1), 215–226.

    Google Scholar 

  26. Sarma, H. K. D., Mall, R., & Kar, A. (2016). E2R2: Energy-efficient and reliable routing for mobile wireless sensor networks. IEEE Systems Journal, 10(2), 604–616.

    Google Scholar 

  27. Xie, G., & Pan, F. (2016). Cluster-based routing for the mobile sink in wireless sensor networks with obstacles. Special section on green communications and networking for 5G wireless. IEEE Access, 4, 2019–2028.

    Google Scholar 

  28. Tan, L., & Mou, W. (2016). Data reduction in wireless sensor networks: A hierarchical LMS prediction approach. IEEE Sensors Journal, 16(6), 1708–1715.

    Google Scholar 

  29. Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honey bees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77–97.

    Google Scholar 

  30. Machado, R., Zhang, W., Wang, G., & Tekinay, S. (2010). Coverage properties of clustered wireless sensor networks. ACM Transactions on Sensor Networks, 7(2), 1–21.

    Google Scholar 

  31. Bajaber, F., & Awan, I. (2014). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication System, 55, 387–401.

    Google Scholar 

  32. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Google Scholar 

  33. Raza, U., Camerra, A., Murphy, A. L., Palpanas, T., & Picco, G. P. (2015). Practical data prediction for real-world wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2231–2244.

    Google Scholar 

  34. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the thirty-third IEEE annual Hawaii international conference on system sciences (pp. 1–10).

  35. Mishra, P., & Dhyani, A. (2015). Proposed framework of LEACH protocol with location based cluster head selection. International Journal of Electronics and Communication Technology, 6(3), 38–40.

    Google Scholar 

  36. 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.

    Google Scholar 

  37. El-Said, S. A., Osamaa, A., & Hassanien, A. E. (2016). Optimized hierarchical routing technique for wireless sensors networks. Soft Computing, 20, 4549–4564.

    Google Scholar 

  38. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., 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.

    Google Scholar 

  39. Han, G., Jiang, J., Guizani, M., & Rodrigues, J. J. C. (2016). Green routing protocols for wireless multimedia sensor networks. IEEE Wireless Communications, 23, 140–146.

    Google Scholar 

  40. Pursley, M. B., Russell, H. B., & Staples, P. E. (1999). Routing for multimedia traffic in wireless frequency-hop communication networks. IEEE Journal on Selected Areas in Communications, 17(5), 784–792.

    Google Scholar 

  41. Lin, K., Rodrigues, J. J. C., Ge, H., Xiong, N., & Liang, X. (2011). Energy efficiency QoS assurance routing in wireless multimedia sensor networks. IEEE Systems Journal, 5(4), 495–505.

    Google Scholar 

  42. Xu, H., Huang, L., Qiao, C., Zhang, Y., & Sun, Q. (2012). Bandwidth-power aware cooperative multipath routing for wireless multimedia sensor networks. IEEE Transactions on Wireless Communications, 11(4), 1532–1543.

    Google Scholar 

  43. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 15(2), 551–591.

    Google Scholar 

  44. Kabir, M. H., Mukhtaruzzaman, M., & Atiquzzaman, M. (2013). Efficient route optimization scheme for nested-NEMO. Journal of Network and Computer Applications, 36, 1039–1049.

    Google Scholar 

  45. Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36, 623–645.

    Google Scholar 

  46. Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35, 1508–1536.

    Google Scholar 

  47. Papadopoulos, A., Navarra, A., McCann, J. A., & Pinotti, C. M. (2012). VIBE: an energy efficient routing protocol for dense and mobile sensor networks. Journal of Network and Computer Applications, 35(4), 1177–1190.

    Google Scholar 

  48. Senouci, M. R., Mellouk, A., Senoucid, H., & Aissani, A. (2012). Performance evaluation of network lifetime spatial–temporal distribution for WSN routing protocols. Journal of Network and Computer Applications, 35(4), 1317–1328.

    Google Scholar 

  49. Mottola, L. (2011). Programming wireless sensor networks: Fundamental concepts and state of the art. Journal ACM Computing Surveys CSUR Surveys, 43(3), 1–51.

    Google Scholar 

  50. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Munuswamy Selvi.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selvi, M., Santhosh Kumar, S.V.N., Ganapathy, S. et al. An Energy Efficient Clustered Gravitational and Fuzzy Based Routing Algorithm in WSNs. Wireless Pers Commun 116, 61–90 (2021). https://doi.org/10.1007/s11277-020-07705-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07705-4

Keywords