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Delay-Sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware (DREAM) Routing Protocol for WSNs

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Abstract

A majority of Wireless Sensor Network (WSN) research at present is focussed on the problems of limited energy supply and its impact on network lifetime. Nevertheless, the plethora of applications conceivable with the help of WSNs often demand for MOO (Multi-Objective Optimization) formulations, where several design goals contend together for the best trade-off solution among them. Therefore, research investigators must also regard other miscellaneous issues in addition to energy efficiency for applicability of WSNs in practical scenarios like Internet of Things. DREAM (Delay-sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware) routing protocol is proposed in the present work, that ameliorates network lifetime (in terms of First Node Death and Last Node Death), throughput (in terms of number of packets sent to Base Station) and latency (average end-to-end delay in seconds) in the network along with enhancing the reliability (in terms of percentage packet loss) of delivered data. The proposed protocol also integrates mobility and heterogeneity of the nodes to cater to the needs of an application-independent general purpose WSN routing protocol, which can be used commercially. Comparative analysis with existing protocols establishes the superiority of the proposed protocol, which is capable of improving the network lifetime by about 3.54% and simultaneously lowering the delay by 35.5%, along with the amelioration of other parameters.

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References

  1. Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless Sensor Networks: Technology, Protocols, and Applications. Wiley Interscience Punlications.

    Book  Google Scholar 

  2. Kumar, S. A., & Ilango, P. (2018). The impact of wireless sensor network in the field of precision agriculture: A review. Wireless Personal Communications, 98(1), 685–698

    Article  Google Scholar 

  3. Ullo, S., Gallo, M., Palmieri, G., Amenta, P., Russo, M., Romano, G., Ferrucci, M., Ferrara, A., De Angelis, M. (2018). Application of wireless sensor networks to environmental monitoring for sustainable mobility. In IEEE International Conference on Environmental Engineering, Milan, Italy.

  4. Biswas, S., Das, R., & Chatterjee, P. (2017). Energy-efficient connected target coverage in multi-hop wireless sensor networks. Industry Interactive Innovations in Science, Engineering and Technology Part of the Lecture Notes in Networks and Systems book series (LNNS), 11, 411–421

    Article  Google Scholar 

  5. Lee, C.-C. (2020). Security and privacy in wireless sensor networks: Advances and challenges. Sensors https://doi.org/10.3390/s20030744

    Article  Google Scholar 

  6. Ehrlich, M., Wisniewski, L., & Jasperneite, J. (2017). State of the art and future applications of industrial wireless sensor networks. Part of the Technologien für die intelligente Automation Book Series (TIA), 2017, 28–39

    Google Scholar 

  7. Aparna, J., Philip, S., & Topkar, A. (2019). Thermal energy harvester powered piezoresistive pressure sensor system with wireless operation for nuclear reactor application. Review of Scientific Instruments, 90(4), 1–10

    Article  Google Scholar 

  8. Zhang, H., Pan, Z., & Zhang, W. (2018). Acoustic–seismic mixed feature extraction based on wavelet transform for vehicle classification in wireless sensor networks. Sensors, 18(6), 1–18

    Article  Google Scholar 

  9. Elhayatmy, G., Dey, N., & Ashour, A. S. (2017). Internet of things based wireless body area network in healthcare. Internet of Things and Big Data Analytics Toward Next-Generation Intelligence Part of the Studies in Big Data Book Series (SBD), 30, 3–20

    Google Scholar 

  10. Wang, J., Jiang, C., Han, Z., Ren, Y., & Hanzo, L. (2018). Internet of vehicles: sensing-aided transportation information collection and diffusion. IEEE Transactions on Vehicular Technology, 67(5), 3813–3825

    Article  Google Scholar 

  11. Modieginyane, K. M., Letswamotse, B. B., Malekian, R., & Abu-Mahfouz, A. M. (2018). Software defined wireless sensor networks application opportunities for efficient network management: A survey. Computers and Electrical Engineering, 66, 274–287

    Article  Google Scholar 

  12. She, C., Yang, C., & Quek, T. Q. S. (2018). Cross-layer optimization for ultra-reliable and low-latency radio access networks. IEEE Transactions on Wireless Communications, 17(1), 127–141

    Article  Google Scholar 

  13. Cheng, L., Niu, J., Luo, C., Shua, L., Kong, L., & Zhiwei Zhao, YuGu. (2018). Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks. Computer Networks, 134, 66–77

    Article  Google Scholar 

  14. Singh, A., Sharma, S., & Singh, J. (2021). Nature-inspired algorithms for wireless sensor networks: A comprehensive survey. Computer Science Review. https://doi.org/10.1016/j.cosrev.2020.100342

    Article  MathSciNet  Google Scholar 

  15. Hao, X., Yao, N., Wang, L., & Wang, J. (2020). Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106470

    Article  Google Scholar 

  16. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In IEEE International Conference on System Sciences, Hawaii.

  17. Smaragdakis, G., Matta, I., Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In International Workshop on Sensor and Actor Network Protocols and Applications (SANPA), Boston.

  18. Kumar, S., Verma, S. K., & Kumar, A. (2015). Enhanced threshold sensitive stable election protocol for heterogeneous wireless sensor networks. Wireless Personal Communications, 85(4), 1–6

    Google Scholar 

  19. Corn, J., Bruce, J. W. (2017). Clustering algorithm for improved network lifetime of mobile wireless sensor networks. In IEEE International Conference on Computing, Networking and Communications, USA.

  20. Sarma, H. K. D., Mall, R., & Kar, A. (2016). E2R2: energy-efficient and reliable routing for mobile wireless sensor networks. IEEE Systems, 10(2), 1–10

    Article  Google Scholar 

  21. Cenedese, A., Luvisotto, M., & Michieletto, G. (2017). Distributed clustering strategies in industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 228–237

    Article  Google Scholar 

  22. Hameed, A.R., Javaid, N., Islam, S., Ahmed, G., Qasim, U., Khan, Z. A. (2016) BEEC: Balanced energy efficient circular routing protocol for underwater wireless sensor networks. In IEEE International Conference on Intelligent Networking and Collaborative Systems, Czech Republic.

  23. Susila, S. G., & Arputhavijayaselvi, J. (2016). Energy proficient reliable rim routing technique for wireless heterogeneous sensor networks lifespan fortification. Circuits and Systems, 7(8), 1751–1759

    Article  Google Scholar 

  24. Akbar, M., Javaid, N., Imran, M., Rao, A., Younis, M. S., & Niaz, I. A. (2016). A multi-hop angular routing protocol for wireless sensor networks. International Journal of Distributed Sensor Networks, 12(9), 1–13

    Article  Google Scholar 

  25. Mirzaie, M., & Mazinani, S. M. (2018). MCFL: An energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network. Wireless Networks, 24(6), 2251–2266

    Article  Google Scholar 

  26. Ouchitachen, H., Hair, A., & Idrissi, N. (2017). Improved multi-objective weighted clustering algorithm in wireless sensor network. Egyptian Informatics Journal, 18(1), 45–54

    Article  Google Scholar 

  27. Sharma, S., Puthal, D., Jena, S. K., Zomaya, A. Y., & Ranjan, R. (2017). Rendezvous based routing protocol for wireless sensor networks with mobile sink. The Journal of Supercomputing, 73(3), 1168–1188

    Article  Google Scholar 

  28. Khalil, E. A., & Ozdemir, S. (2017). Reliable and energy efficient topology control in probabilistic wireless sensor networks via multi-objective optimization. Journal of Supercomputers, 73(6), 2632–2656

    Article  Google Scholar 

  29. Dong, M., Ota, K., Liu, A., & Guo, M. (2016). Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks. IEEE Transactions on Parallel And Distributed Systems, 27(1), 225–236

    Article  Google Scholar 

  30. Kaur, S.D.G., Agrawal, S., Vig, R. (2018) Energy efficient sector-based clustering protocol (EESCP) for heterogeneous WSN. In Springer LNNS Series 2nd International Conference on Communication, Computing and Networking, National Institute of Technical Teachers Training and Research, Chandigarh, March 2018.

  31. Dutt, S., Agrawal, S., & Vig, R. (2018). Cluster-head restricted energy efficient protocol (CREEP) for routing in heterogeneous wireless sensor networks. Wireless Personal Communications, 100(4), 1477–1497

    Article  Google Scholar 

  32. Dutt, S., Shandil, N., Agrawal, S., & Vig, R. (2019). Delay aware and lifetime enhanced sectoring (DALES) algorithm for routing in heterogeneous wireless sensor network. International Journal of Sensors Wireless Communication and Control. https://doi.org/10.2174/2210327909666190219124707

    Article  Google Scholar 

  33. Dutt, S., Agrawal, S., & Vig, R. (2019). Impact of variable packet length on the performance of heterogeneous multimedia wireless sensor networks. Wireless Personal Communications, 107(4), 1849–1863

    Article  Google Scholar 

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Authors and Affiliations

  1. Chandigarh University, Mohali, India

    Suniti Dutt

  2. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Sunil Agrawal & Renu Vig

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  1. Suniti Dutt
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  2. Sunil Agrawal
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  3. Renu Vig
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Correspondence to Suniti Dutt.

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Dutt, S., Agrawal, S. & Vig, R. Delay-Sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware (DREAM) Routing Protocol for WSNs. Wireless Pers Commun 120, 1675–1703 (2021). https://doi.org/10.1007/s11277-021-08528-7

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