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Energy Efficient Dynamic Sink Multi Level Heterogeneous Extended Distributed Clustering Routing for Scalable WSN: ML-HEDEEC

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Abstract

The size of the wireless sensor network (WSN) is extending continually with use of IOT networks. The main difficulty for design wide area WSN is to maintain the higher stability period and energy efficiency (EE) for the routing protocols. The creation of clustering-based routing protocols was applied to the optimization of overall network energy. But, traditional clustering methods were unable to produce improved node heterogeneity, and extended network lifetime. Distributed clustering-based routing protocols are specially designed for enhancing the EE of the networks. In addition, EE can be improved by enhancing the heterogeneity of the node distribution. This paper aims to design the extended distributed clustering-based EE routing protocol. The heterogeneity of nodes is improved by introducing the additional intermediate advanced nodes layer in the network. Therefore, paper proposed to design the Multi-Level Heterogynous EDEEC rousing protocol called ML-HEDEEC by adapting optimum energy enhancing parameters. The notes are divided to normal, advance, advance-interdicted and supper nodes based on the energy allocated to them. The probability of nodes is modified for better clustering and cluster head election by introducing additional energy enhancement parameter. In addition, it is proposed to automatically adopt the network initial energy based on the scaling of network dimensions. This may lead to enhance EE of the network and may improve stability period. Finally, the results are evaluated for a case of WSN routing under the dynamic sink locations. Performance is compared for various distributed clustering protocols and other state of art protocols viz. LEACH, SEP, zonal- SEP, DEC considering the network scaling. Various performances of the network stability, packets sent to base station, and lifetime,.are defined for result evaluation. The network dimensions are scaled up to four times and proposed protocol is tested under scaling consideration. In addition, sink locations are also varied for dynamic sink locations performance evaluation. Overall paper efficiently designed and test heterogeneous improved routing protocol with extended lifetime and stability.

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Data Availability

The data query is generated through MATLAB simulator for evaluation purpose.

Code Availability

The code for different existing protocol is available at MATLAB repository. By changing different parameter related to sink position and number of nodes in a particular sensor area the comparative study and statistics presented in the research work.

References

  1. Ahmed Elsmany, E. F., Omar, M. A., Wan, T.-C., & Altahir, A. A. (2019). EESRA: Energy efficient scalable routing algorithm for wireless sensor networks. IEEE Access, 7, 96974–96983.

    Article  Google Scholar 

  2. Daas, M. S., Chikhi, S., & Bourennane, E.-B. (2021). A dynamic multi-sink routing protocol for static and mobile self-organizing wireless networks: A routing protocol for Internet of Things. Ad Hoc Networks, 117(102495), 1–16.

    Google Scholar 

  3. Khelifi, N., Nataf, E., Oteafy, S., & Youssef, H. (2018). RESCUE-SINK: Dynamic sink augmentation for RPL in the Internet of Things. Transactions on Emerging Telecommunications Technologies, 29(2), 1.

    Article  Google Scholar 

  4. Njoya, A. N., Thron, C., Barry, J., Abdou, W., Tonye, E., Konje, N. S. L., & Dipanda, A. (2017). Efficient scalable sensor node placement algorithm for fixed target coverage applications of wireless sensor networks. IET Wireless Sensor Systems, 7(2), 44–54.

    Article  Google Scholar 

  5. Pakzad, S. N., Fenves, G. L., Kim, S., & Culler, D. E. (2008). Design and implementation of scalable wireless sensor network for structural monitoring. Journal of Infrastructure Systems, 1089, 1–14.

    Google Scholar 

  6. Mirza, M. A., Shakir, M. Z., & Alouini, M.-S. (2013). A scalable global positioning system-free localization scheme for underwater wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2013, 1–10.

    Article  Google Scholar 

  7. Abutu, I. M., Imeh, U. J., Abdoulie, T. M. S., Adewale, A. E., & Bashir, M. M. (2018). Real time universal scalable wireless sensor network for environmental monitoring application. I. J. Computer Network and Information Security, 6, 68–75.

    Article  Google Scholar 

  8. Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of wireless sensor networks: An up-to-date survey. Applied System Innovation, 3(14), 1.

    Google Scholar 

  9. Ahmad, A., Rathore, M. M., Paul, A., & Chen, B.-W. (2015). Data Transmission Scheme Using Mobile Sink in Static Wireless Sensor Network. Journal of Sensor, 2015, 1.

    Article  Google Scholar 

  10. Randhawa, S., & Jain, S. (2017). Data aggregation in wireless sensor networks: Previous research, current status and future directions wireless personal communication. Springer.

  11. Zhang, J., Hu, P., Xie, F., Long, J., & He, A. (2018). An energy efficient and reliable in-network data aggregation scheme for WSN. IEEE Access, 6, 71857–71870.

    Article  Google Scholar 

  12. Sasirekha, S., & Swamynathan, S. (2017). A comparative study and analysis of data aggregation techniques in WSN. Indian Journal of Science and Technology, 8(26), 1–10.

    Google Scholar 

  13. Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: Development of energy efficient LEACH protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking, 76, 1.

    Google Scholar 

  14. Khurana, B., & P., & Kant, K. (2016). LEACH-MAC: A new cluster head selection algorithm for wireless sensor networks. Journal of Wireless Networks, 22, 49–60.

    Article  Google Scholar 

  15. Shah, T., Javaid, N., & Qureshi, T. N. (2012). Energy efficient sleep awake aware (EESAA) intelligent sensor network routing protocol. In 2012 15th International Multitopic Conference (INMIC), Islamabad (pp. 317–322).

  16. Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Application, 46, 198–226.

    Article  Google Scholar 

  17. Smaragdakis, G., Matta, I., Bestavros, A. (2004). SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks. Second International Workshop, on Sensor and Actor Network Protocols and Applications (SANPA 2004).

  18. Kaur, G., Rani, S., & Kakkar, S. (2016). Design of an improved DEC protocol for wireless sensor networks. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 5(4), 1.

    Google Scholar 

  19. Lindsey, S., & Raghavendra, C. S. (2012). PEGASIS: Power efficient gathering in sensor information system. IEEE Aerospace Conference Proceedings, 3, 1125–1130.

    Google Scholar 

  20. Marhoon, H. A., Mahmuddin, M., & Nor, S. A. (2015). Chain based routing protocols in wireless sensor networks. ARPN Journal of Engineering and Applied Sciences, 10(3), 1.

    Google Scholar 

  21. Vaidya, R., & Dandekar, D. R. (2013). Comparison of SPAN and LEACH protocol for topology control in wireless sensor networks. IEEE International conference on signal processing, image processing & pattern recognition.

  22. Kaur, S., & Vashisht, R. (2014). Hybrid approach of data aggregation (HADA) based on iLEACH in WSNs. American Journal of Advanced Computing, I(2), 24–30.

    Google Scholar 

  23. Bin, H. E., Hongtao, Z. (2013). An Energy Optimization Method For Wireless Sensor Network” 27th International Conference On Advanced Information Networking And Applications Workshops (WAINA).

  24. Parmar, B., Munjani, J., Meisuria, J., & Singh, A. (2014). A Survey of routing protocol LEACH for WSN. International Journal of Scientific and Research Publications, 4(1), 1.

    Google Scholar 

  25. Chagas, S. H., Martins, J. B., & de Oliveira, L. L. (2012) An approach to localization scheme of wireless sensor networks based on artificial neural networks and Genetic Algorithms. IEEE 10th International conference on New Circuits and Systems Conference (NEWCAS).

  26. Aderohunmu, F. A., & Deng, J. D. (2009) An Enhanced Stable Election Protocol (SEP) for Clustered Heterogeneous WSN. IEEE workshop available at research Gate.

  27. Jagadeesh Naik, L., Ramanaiah, K. V., & Soundara Rajan, K. (2019). Performance evaluation of MCHSEP and SEP protocol for wireless sensor networks. International Journal of Recent Technology and Engineering (IJRTE), 7, 1.

    Google Scholar 

  28. Sharma, U., & Tiwari, S. (2014). Performance analysis of SEP and LEACH for heterogeneous wireless sensor networks. International Journal of Computer Trends and Technology (IJCTT), 10(4), 1.

    Google Scholar 

  29. Singh, P. K., Yadav, D. K., & Dixit, S. (2015). Modified stable election protocol (M-SEP) for wireless sensor network. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), 4(4), 1.

    Google Scholar 

  30. Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. ISSNIP an IEEE International Symposium., 1, 341–346.

    Google Scholar 

  31. Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29, 22302237.

    Article  Google Scholar 

  32. Prasada Reddy, M. M., & Varada Rajan, S. (2017). DEEC protocol for WSNs. Advances in Wireless and Mobile Communications., 10(1), 51–63.

    Google Scholar 

  33. Sharma, D., & Tomar, G. S. (2021). Energy efficient multitier random DEC routing protocols for WSN. Agricultural Wireless Personal Communications, 120, 727–747.

    Article  Google Scholar 

  34. Javaid, N., Qureshi, T. N., Khan, A. H., & Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks. arXiv preprint arXiv:1303.5274.

  35. Sharma, A. K., & Kourtney, H. (2010). Hybrid energy efficient distributed protocol for heterogeneous wireless sensor network. International Journal of Computer Applications, 4(6), 1–5.

    Article  Google Scholar 

  36. Redjimi, K., & Redjim, M. (2022). The DEEC and EDEEC Heterogeneous WSN Routing Protocols. International Journal on Advanced Networking and Applications, 13(4), 5045–5051.

    Article  Google Scholar 

  37. Elbhiri, B., Rachid, S., Elfkihi, S., Aboutajdine, D. (2010). Developed Distributed Energy-Efficient clustering (DDEEC) for heterogeneous wireless sensor networks. In 5th International Symposium on I/V Communications and Mobile Networks (ISVC). ISBN 978-1-4244-5996-4.

  38. Priya, R. (2018). EDEEC-enhanced distributed energy efficient clustering protocol for heterogeneous wireless sensor network (WSN) scheme for WSN. IEEE Access.

  39. Zanjireh, M. M., & Larijani, H. (2015). A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs. 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) (pp. 1–6).

  40. Qureshi, T. N., Javaid, N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2018). BEENISH: Balanced energy efficient network integrated super heterogenous protocol for wireless sensor networks. Elsevier Procedia Computer Science, 1, 1.

    Google Scholar 

  41. Uniyal, N., Thakkar, V. M., & Bahuguna, A. (2017) Enhanced Energy Zonal Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Network. IEEE 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) 2017.

  42. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2020). I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring. IEEE Internet of Things Journal, 7(1), 710–717.

    Article  Google Scholar 

  43. Jia, D., Zhu, H., Zou, S., & Hu, P. (2016). Dynamic Cluster Head Selection Method for Wireless Sensor Network. IEEE Sensors Journal, 16(8), 1.

    Article  Google Scholar 

  44. Karthik Reddy, G., & Nirmala Devi, L. (2018). A Review on Clustering Protocols with Energy heterogeneity in Wireless Sensor Networks. IEEE International Conference on Communication, Computing and Internet of Things (IC3IoT Nov 2018).

  45. Bhola, J., Shabaz, M., Dhiman, G., et al. (2021). Performance evaluation of multilayer clustering network using distributed energy efficient clustering with enhanced threshold protocol. Wireless Personal Communication, 1, 1–15. https://doi.org/10.1007/s11277-021-08780-x

    Article  Google Scholar 

  46. Zhong, X., & Liang, Y. (2018). Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation. 2018 IEEE 43rd Conference on Local Computer Networks (LCN), Chicago, IL, USA (pp. 275–278).

  47. El Khediri, S., Fakhet, W., Moulahid, T., Khand, R., Thaljaouie, A., & Kachouric, A. (2020). Improved node localization using K-means clustering for Wireless Sensor Networks. Computer Science Review, 37, 1.

    Article  MathSciNet  Google Scholar 

  48. Mishra, S. N., Elappila, M., & Chinara, S. (2018). Development of Survival Path Routing Protocol for Scalable Wireless Sensor Networks. In 2018 International Conference on Information Technology (ICIT), Bhubaneswar, India (pp. 210–215).

  49. Sandhya, R., & Sengottaiyan, N. (2016) S-SEECH secured - Scalable Energy Efficient Clustering Hierarchy Protocol for Wireless Sensor Network. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, 2016 (pp. 306–309).

  50. Sah, D. K., & Amgoth, T. (2018). Parametric survey on cross-layer designs for wireless sensor networks. Computer Science Review, 27, 112–134.

    Article  MathSciNet  Google Scholar 

  51. Singh, O., & Rishiwal, V. (2017). On the scalability of routing protocols in WSN. In 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall), Dehradun (pp. 1–6).

  52. Rani, P., & Sharma, A. (2021) Linear scalable routing protocol for wireless sensor network. IOP Conference Series: Materials Science and Engineering, 1057. 012094.

  53. Shukla, A., & Tripathi, S. (2020). A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network. Journal of Wireless Network Springer, 9, 1.

    Google Scholar 

  54. Yadav, A., & Kumar, S. (2016). An Enhanced Distributed Energy-Efficient Clustering (DEEC) Protocol for Wireless Sensor Networks. International Journal of Future Generation Communication and Networking, 9(11), 49–58.

    Article  Google Scholar 

  55. Maitra, T., & Roy, S. (2016). A comparative study on popular MAC protocols for mixed Wireless Sensor Networks: From implementation viewpoint. Computer Science Review, 22, 107–134.

    Article  MathSciNet  Google Scholar 

  56. Singh, S., Malik, A., & Kumar, R. (2017). Energy efficient heterogeneous DEEC protocol for enhancing lifetime in WSNs. Engineering Science and Technology, an International Journal, 20(1), 345–353.

    Article  Google Scholar 

  57. Vançinand, S., & Erdem, E. (2018). Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2018, 1.

    Article  Google Scholar 

  58. Khan, M. Y., Javaid, N., Khan, M., Javaid, A., Khan, Z., Qasim, U. (2013). Hybrid DEEC: Towards Efficient Energy Utilization in Wireless Sensor Networks arXiv preprint arXiv: 1303.4679

  59. Sheenam. (2015). G-DEEC: Gateway based multi-hop distributed energy efficient clustering protocol for wireless sensor networks. International Journal on Cybernetics & Informatics (IJCI), 4(5), 1.

    Google Scholar 

  60. Huang, J., Zhao, Z., Yuan, Y., et al. (2017). Multi-factor and distributed clustering routing protocol in wireless sensor networks. Wireless Personal Communication, 95, 2127–2142.

    Article  Google Scholar 

  61. Gupta, S. K., & Singh, S. (2022). Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication. International Journal of Communication Systems, 35(11), e5194.

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technical Teachers Training and Research, Bhopal, Madhya Pradesh, 462002, India

    Susheel Kumar Gupta

  2. Department of CSE, National Institute of Technical Teachers Training and Research, Bhopal, Madhya Pradesh, 462002, India

    Shailendra Singh

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Gupta, S.K., Singh, S. Energy Efficient Dynamic Sink Multi Level Heterogeneous Extended Distributed Clustering Routing for Scalable WSN: ML-HEDEEC. Wireless Pers Commun 128, 559–585 (2023). https://doi.org/10.1007/s11277-022-09967-6

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