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CBPR: A Cluster-Based Backpressure Routing for the Internet of Things

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Abstract

The concept of the Internet of Things (IoT)-based Wireless Sensor Network (WSN) is rapidly gaining wide-spread recognition and acceptance in our day-to-day lives. Nowadays, the application of the IoT sensor nodes in various domains of endeavors such as health-care, smart homes, industrial and production sectors, control networks, and in many other fields has continued to increase steadily. In IoT-based WSN, sensor nodes dynamically join the internet and collaborate to accomplish a mission by sensing and collecting event data from the application field. The sensor nodes thus forward the collected information to the sink nodes or to the nearest base station for further transmission. However, one of the significant drawbacks of the IoT-based WSN networks is that the battery life of the sensor nodes is often short-lived due to the energy-limited nature of the electronic sensors, resulting in the network’s short lifetime. Thus, prolonging the lifetime duration of the sensor nodes becomes a fundamental task. Whether the battery life of a sensor node is extended for a reasonable length of time or depleted in a moment depends mainly on the energy efficiency of the underlying routing protocol. Therefore, the issue of network lifetime can be fundamentally addressed by implementing an efficient and robust energy-aware routing protocol for sustainable and prolonged network operation time in a WSN based IoT network scenario. In this paper, a cluster-based Backpressure routing (CBPR) scheme has been proposed, which targets to prolong the network lifetime and enhance the data transmission reliability using energy load-balancing mechanism. For every cluster of the sensor node, the CBPR scheme elects a cluster head which has the highest energy level and the shortest distance to the sink node. The proposed CBPR routing scheme further utilizes a very robust data aggregation algorithm to checkmate and prevent the circulation of redundant data packets in the network while also exploiting the Backpressure scheduling machine for data packets queueing and for route selection, which allows it to select the next-hop sensor node based on the queue length value of the sensor nodes. Extensive simulations have been performed to evaluate the efficiency of the proposed CBPR routing scheme, which was compared with that of other well-known routing schemes such as the Information Fusion Based Role Assignment and Data Routing for In-Network Aggregation in terms of throughput, energy consumption, and packet delivery ratio.

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References

  1. Ray, P. P., Mukherjee, M., & Shu, L. (2017). Internet of things for disaster management: State-of-the-art and prospects. IEEE Access, 5, 18818–18835. https://doi.org/10.1109/ACCESS.2017.2752174.

    Article  Google Scholar 

  2. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 20(1), 416–464. https://doi.org/10.1109/COMST.2017.2771153.

    Article  Google Scholar 

  3. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    Article  Google Scholar 

  4. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.

    Article  Google Scholar 

  5. Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017). A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access, 5, 3302–3312.

    Article  Google Scholar 

  6. Malathy, S. et al. (2020). “An optimal network coding based backpressure routing approach for massive IoT network”. Wireless Networks, pp. 1–18.

  7. Amiri, I. S., et al. (2020). DABPR: A large-scale internet of things-based data aggregation back pressure routing for disaster management. Wireless Networks, 26(4), 2353–2374.

    Article  MathSciNet  Google Scholar 

  8. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. https://doi.org/10.1109/TWC.2002.804190.

    Article  Google Scholar 

  9. Boulfekhar, S., & Benmohammed, M. (2013). A novel energy efficient and lifetime maximization routing protocol in wireless sensor networks. Wireless personal communications, 72(2), 1333–1349.

    Article  Google Scholar 

  10. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). "Energy-efficient communication protocol for wireless microsensor networks," IEEE, pp. 10-pp.

  11. Dener, M. (2018). A new energy efficient hierarchical routing protocol for wireless sensor networks. Wireless Personal Communications, 101(1), 269–286.

    Article  Google Scholar 

  12. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. IEEE, 3, 3–3.

    Google Scholar 

  13. Maheswar, R. & Jayaparvathy, R. (2010). "Performance analysis using contention based queueing model for wireless sensor networks," The International Congress for global Science and Technology, pp. 59–59.

  14. Jayarajan, P., Maheswar, R., Sivasankaran, V., Vigneswaran, D., & Udaiyakumar,R. (2018). "Performance analysis of contention based priority queuing model using N-policy model for cluster based sensor networks," IEEE, pp. 0229–0233.

  15. Maheswar, R., & Jayaparvathy, R. (2011). Performance analysis of cluster based sensor networks using N-policy M/G/1 queueing model. Eur. J. Scientific Research, 58(2), 177–188.

    Google Scholar 

  16. Jayarajan, P., Maheswar, R., & Kanagachidambaresan, G. R. (2019). Modified energy minimization scheme using queue threshold based on priority queueing model. Cluster Computing, 22(5), 12111–12118.

    Article  Google Scholar 

  17. Maheswar, R., & Jayaparvathy, R. (2012). Performance analysis of fault tolerant node in wireless sensor network (pp. 121–126). New York: Springer.

    Google Scholar 

  18. Jayarajan, P., Maheswar, R., Kanagachidambaresan, G. R., Sivasankaran, V., Balaji, M., & Das, J. (2018). "Performance evaluation of fault nodes using queue threshold based on N-policy priority queueing model," IEEE, pp. 1–5.

  19. Laktharia, K. I., Kanagachidambaresan, G. R., Maheswar, R., Mahima, V., & Darwish, A. (2017). "Buffer capacity based node life time estimation in wireless sensor network," IEEE, pp. 1–5.

  20. Nageswari, D., Maheswar, R., & Kanagachidambaresan, G. R. (2019). Performance analysis of cluster based homogeneous sensor network using energy efficient N-policy (EENP) model. Cluster Computing, 22(5), 12243–12250.

    Article  Google Scholar 

  21. Maheswar, R., Jayarajan, P., Vimalraj, S., Sivagnanam, G., Sivasankaran, V., & Amiri, I. S. (2018). "Energy efficient real time environmental monitoring system using buffer management protocol," IEEE, pp. 1–5.

  22. Jayarajan, P., Kanagachidambaresan, G. R., Sundararajan, T. V. P., Sakthipandi, K., Maheswar, R., & Karthikeyan, A. (2020). An energy-aware buffer management (EABM) routing protocol for WSN. The Journal of Supercomputing, 76(6), 4543–4555.

    Article  Google Scholar 

  23. Nakamura, E. F., de Oliveira, H. A. B. F., Pontello, L. F., & Loureiro, A. A. F. (2006). "On demand role assignment for event-detection in sensor networks," In 11th IEEE symposium on computers and communications (ISCC'06), 26–29, pp. 941–947, https://doi.org/10.1109/ISCC.2006.110.

  24. Villas, L. A., Boukerche, A., Ramos, H. S., de Oliveira, H. A. B. F., de Araujo, R. B., & Loureiro, A. A. F. (2013). DRINA: A lightweight and reliable routing approach for in-network aggregation in wireless sensor networks. IEEE Transactions on Computers, 62(4), 676–689.

    Article  MathSciNet  Google Scholar 

  25. Tilwari, V., Dimyati, K., Hindia, M. H. D., Fattouh, A., & Amiri, I. S. (2019). Mobility, residual energy, and link quality aware multipath routing in MANETs with Q-learning algorithm. Applied Sciences, 9(8), 1582.

    Article  Google Scholar 

  26. Tilwari, V., et al. (2020). MCLMR: A multicriteria based multipath routing in the mobile ad hoc networks. Wireless Personal Communications, 112(4), 2461–2483.

    Article  Google Scholar 

  27. Tilwari, V., Dimyati, K., Hindia, M. H. D., Noor Izam, T. F. B. T. M., & Amiri, I. S. (2020). EMBLR: A high-performance optimal routing approach for D2D communications in large-scale IoT 5G network. Symmetry, 12(3), 438.

    Article  Google Scholar 

  28. Tilwari, V., Hindia, M. N., Dimyati, K., Qamar, F., Talip, A., & Sofian, M. (2019). Contention window and residual battery aware multipath routing schemes in mobile ad-hoc networks. International Journal of Technology, 10(7), 1376–1384.

    Article  Google Scholar 

  29. Rappaport, T. S. (1996). Wireless communications: Principles and practice. New Jersey: Prentice Hall PTR.

    MATH  Google Scholar 

  30. Hu, S., & Han, J. (2014). Power control strategy for clustering wireless sensor networks based on multi-packet reception. IET Wireless Sensor Systems, 4(3), 122–129.

    Article  Google Scholar 

  31. Zheng, H., Yang, F., Tian, X., Gan, X., Wang, X., & Xiao, S. (2015). Data gathering with compressive sensing in wireless sensor networks: A random walk based approach. IEEE Transactions on Parallel and Distributed Systems, 26(1), 35–44.

    Article  Google Scholar 

  32. Luo, D., Zhu, X., Wu, X., & Chen, G. (2011). "Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks," IEEE, pp. 1566–1574.

  33. Hai, L., Wang, J., Wang, P., Wang, H., & Yang, T. (2017). High-throughput network coding aware routing in time-varying multihop networks. IEEE Transactions on Vehicular Technology, 66(7), 6299–6309.

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the EPSRC grant EP/P028764/1 (UM IF035-2017) and Fundamental Research Grant Scheme (FRGS) (FP024-2020) for supporting this work.

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Authors and Affiliations

  1. School of EEE, VIT Bhopal University, Bhopal, India

    R. Maheswar

  2. Department of ECE, Sri Krishna College of Technology, Coimbatore, India

    P. Jayarajan

  3. School of CSE, VIT Bhopal University, Bhopal, India

    A. Sampathkumar

  4. Department of CSE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Morai, India

    G. R. Kanagachidambaresan

  5. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    M. H. D. Nour Hindia, Valmik Tilwari, Kaharudin Dimyati & Henry Ojukwu

  6. Computational Optics Research Group, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Iraj Sadegh Amiri

  7. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Iraj Sadegh Amiri

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  1. R. Maheswar
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  2. P. Jayarajan
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  3. A. Sampathkumar
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  9. Iraj Sadegh Amiri
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Correspondence to Iraj Sadegh Amiri.

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Maheswar, R., Jayarajan, P., Sampathkumar, A. et al. CBPR: A Cluster-Based Backpressure Routing for the Internet of Things. Wireless Pers Commun 118, 3167–3185 (2021). https://doi.org/10.1007/s11277-021-08173-0

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