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Energy Efficient SOCGO Protocol for Hole Repair Node Scheduling in Reliable Sensor System

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Abstract

A sensor node in the wireless sensor network (WSN) has an inadequate energy, and it cannot be interchanged due to the arbitrary placement, so the objective is to extend the network lifetime. An inadequate energy becomes a crucial problem to solve energy efficiency in WSN. In this work, we propose a new Self-Organizing Cluster based Greedy best-first search Opportunistic routing (SOCGO) protocol for balanced energy routing. In our proposed work, the operation of work is divided into four stages for energy balanced routing. They are Sleep state, active state, guard state and death state. The residual energy consumed through the examining adjacent nodes is presented as an aspect to estimate the detection rate, and it can achieve through the novel approach named as hybrid K-means and Greedy best-first search algorithm. Furthermore, the presence of coverage holes pairs within WSN can be recovered through an Opportunistic routing algorithm. This method repairs the coverage hole pairs and improves the network lifetime. Then this proposed SOCGO protocol can be implemented in both the homogeneous and heterogeneous environment. Simulation outcomes deliberates that our proposed SOCGO algorithm attains enhanced performance regarding the network lifetime, throughput, energy consumption, average end to end delay and packet delivery ratio.

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References

  1. Khalil, E. A., & Bara’a, A. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, elsevier,1(4), 195–203.

    Article  Google Scholar 

  2. Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences,2(1), 13.

    Article  Google Scholar 

  3. Lung, C.-H., & Zhou, C. (2010). Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach. Ad Hoc Networks,8(3), 328–344.

    Article  Google Scholar 

  4. Bao, F., Chen, R., Chang, M. J., & Cho, J.-H. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE Transactions on Network and Service Management,9(2), 169–183.

    Article  Google Scholar 

  5. Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications,32(4), 662–667.

    Article  Google Scholar 

  6. Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, E. A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks. Procedia Computer Science, Elsevier,19, 914–919.

    Article  Google Scholar 

  7. Qureshi, T. N., Javaid, N., Khan, A. H., Iqbal, E., Akhtar, A., & Ishfaq, M. (2013). BEENISH: Balanced energy efficient network integrated super heterogeneous protocol for wireless sensor networks. Procedia Computer Science,19, 920–925.

    Article  Google Scholar 

  8. Israr, N., & Awan, I. (2008). Coverage based inter cluster communication for load balancing in heterogeneous wireless sensor networks. Telecommunication Systems, Springer,38(3), 121–132.

    Article  Google Scholar 

  9. 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(2), 623–645.

    Article  Google Scholar 

  10. Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, Springer,18(7), 847–860.

    Article  Google Scholar 

  11. Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with non-uniform node distribution. AEU-International Journal of Electronics and Communications,66(1), 54–61.

    Article  Google Scholar 

  12. Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications,31(14), 3451–3459.

    Article  Google Scholar 

  13. Hoang, D. C., Yadav, P., Kumar, R., & Panda, S. K. (2014). Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Transactions on Industrial Informatics,10(1), 774–783.

    Article  Google Scholar 

  14. Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, Elsevier,33, 127–140.

    Article  Google Scholar 

  15. Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal,15(8), 4576–4586.

    Article  Google Scholar 

  16. Wang, A., Yang, D., & Sun, D. (2012). A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks. Computers & Electrical Engineering,38(3), 662–671.

    Article  Google Scholar 

  17. Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems,4(1), 9–16.

    Google Scholar 

  18. Zhang, P., Xiao, G., & Tan, H.-P. (2013). Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors. Computer Networks, Elsevier,57(14), 2689–2704.

    Article  Google Scholar 

  19. Tarhani, M., Kavian, Y. S., & Siavoshi, S. (2014). SEECH: Scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sensors Journal,14(11), 3944–3954.

    Article  Google Scholar 

  20. Li, J. H., Bhattacharjee, B., Yu, M., & Levy, R. (2008). A scalable key management and clustering scheme for wireless ad hoc and sensor networks. Future Generation Computer Systems, Elsevier,24(8), 860–869.

    Article  Google Scholar 

  21. Iqbal, S., Kiah, M. L., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Albahri, A. S., et al. (2018). Real-time-based E-health systems: design and implementation of a lightweight key management protocol for securing sensitive information of patients. Health and Technology,9, 93–111.

    Article  Google Scholar 

  22. Meena, U., & Sharma, A. (2018). Secure key agreement with rekeying using FLSO routing protocol in wireless sensor network. Wireless Personal Communications,101, 1177–1199.

    Article  Google Scholar 

  23. Zhu, J., Lung, C.-H., & Srivastava, V. (2015). A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks. Ad Hoc Networks, Elsevier,25, 38–53.

    Article  Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. Faheem, M., Abbas, M. Z., Tuna, G., & Gungor, V. C. (2015). EDHRP: Energy efficient event driven hybrid routing protocol for densely deployed wireless sensor networks. Journal of Network and Computer Applications,58, 309–326.

    Article  Google Scholar 

  26. Leu, J.-S., Chiang, T.-H., Yu, M.-C., & Su, K.-W. (2015). Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Communications Letters,19(2), 259–262.

    Article  Google Scholar 

  27. Sabet, M., & Naji, H. R. (2015). A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU-International Journal of Electronics and Communications,69(5), 790–799.

    Article  Google Scholar 

  28. Sharma, Suraj, & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review,45(2), 14–20.

    Article  Google Scholar 

  29. Jin, R. C., Gao, T., Song, J. Y., Zou, J. Y., & Wang, L. D. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks,19(8), 1851–1866.

    Article  Google Scholar 

  30. Kavidha, V., & Ananthakumaran, S. (2018). Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink. Peer-to-Peer Networking and Applications,12, 881–892.

    Article  Google Scholar 

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Correspondence to Seema Dahiya.

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Dahiya, S., Singh, P.K. Energy Efficient SOCGO Protocol for Hole Repair Node Scheduling in Reliable Sensor System. Wireless Pers Commun 110, 445–465 (2020). https://doi.org/10.1007/s11277-019-06736-w

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