Optimal network dimensions for energy conservation in clustered 3D WSN

Abstract

In this paper, the optimal network dimensions of clustered-routing three-dimensional (3D) wireless sensor networks is provided. The derivation for such dimensions is based upon the minimum energy consumption cost function in the network. Two 3D network geometries are considered, namely, cuboid and cylinder networks. Analysis and simulations have shown that the minimum energy consumption of the two 3D geometries occurs in special dimensions setup. First, for the cuboid network the optimal network dimensions occur at equal length width and height. We refer to this network as cube network. Second, for the cylinder network the optimal case occurs when the radius of the network is around 68.4% of its height. The results are verified using simulations. Low energy adaptive clustering hierarchy protocol has been used as the underlining routing protocol in 3D environment. Total network remaining energy, stable region and network throughput are utilized for performance evaluation of the results. It is shown that networks with optimal dimensions achieve maximum network lifetime and throughput with minimum energy consumption. Moreover, in the optimal network dimensions settings the cylinder network utilizes energy more efficiently and outperforms the cube network in terms of network lifetime and throughput.

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References

  1. 1.

    Barani, H., Jaradat, Y., Huang, H., Li, Z., & Misra, S. (2018). Effect of sink location and redundancy on multi-sink wireless sensor networks: A capacity and delay analysis. IET Communications, 8(12), 941–947.

    Article  Google Scholar 

  2. 2.

    Jaradat, Y., Masoud, M., Jannoud, I., Abu-Sharar, T., & Zerek, A. (2019). Performance Analysis of Homogeneous LEACH Protocol in Realistic Noisy WSN. In 2019 19th international conference on sciences and techniques of automatic control and computer engineering (STA) (pp. 590–594). IEEE.

  3. 3.

    Rodgers, M., Pai, V., & Conroy, R. (2015). Recent advances in wearable sensors for health monitoring. IEEE Sensors Journal, 15(6), 3119–26.

    Article  Google Scholar 

  4. 4.

    Xia, C., Liu, W., & Deng, Q. (2015). Cost minimization of wireless sensor networks with unlimited-lifetime energy for monitoring oil pipelines. IEEE/CAA Journal of Automatica Sinica, 2(3), 290–295.

    MathSciNet  Article  Google Scholar 

  5. 5.

    Singh, S., Kumar, P., & Singh, J. (2017). A survey on successors of LEACH protocol. IEEE Access, 5, 4298–328.

    Article  Google Scholar 

  6. 6.

    Alrabea, A., Alzubi, O. A., & Alzubi, J. A. (2019). A task-based model for minimizing energy consumption in WSNs. Energy Systems, 1–18.

  7. 7.

    Boyinbode, O., Hanh, L., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2–3), 130–136.

    Article  Google Scholar 

  8. 8.

    Jain, N., Sinha, P., & Gupta, S. K. (2013). Clustering protocols in wireless sensor networks: A survey. International Journal of Applied Information System, 5(2), 41–50.

    Google Scholar 

  9. 9.

    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In IEEE Proceedings of the 33rd annual Hawaii international conference on system sciences (p. 10).

  10. 10.

    Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston: Boston University Computer Science Department.

    Google Scholar 

  11. 11.

    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 4, 366–379.

    Article  Google Scholar 

  12. 12.

    Jaradat, Y., Masoud, M., & Al-Jazzaar, S. (2020). A comparative study of the effect of node distributions on 2D and 3D heterogeneous WSN. International Journal of Sensor Networks, 33(4), 202–210.

    Article  Google Scholar 

  13. 13.

    Baghouri, M., Hajraoui, A., & Chakkor, S. (2017). Stable election protocol for three dimensional clustered heterogeneous wireless sensor network. WSEAS Transactions on Communications, 16, 298–305.

    Google Scholar 

  14. 14.

    Ojha, T., Misra, S., & Raghuwanshi, N. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66–84.

    Article  Google Scholar 

  15. 15.

    Moridi, M., Sharifzadeh, M., Kawamura, Y., & Jang, H. D. (2018). Development of wireless sensor networks for underground communication and monitoring systems (the cases of underground mine environments). Tunnelling and Underground Space Technology, 73, 127–138.

    Article  Google Scholar 

  16. 16.

    Mohapatra, A., Gautam, N., & Gibson, R. (2012). Combined routing and node replacement in energy-efficient underwater sensor networks for seismic monitoring. IEEE Journal of Oceanic Engineering, 38(1), 80–90.

    Article  Google Scholar 

  17. 17.

    Morozs, N., Mitchell, P., & Zakharov, Y. (2018). Unsynchronized dual-hop scheduling for practical data gathering in underwater sensor networks. In 2018 fourth underwater communications and networking conference (UComms) (pp. 1–5). IEEE.

  18. 18.

    Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. (2015). Cross-layer network lifetime maximization in interference-limited WSNs. IEEETransactions on Vehicular Technology, 64(8), 3795–3803.

    Article  Google Scholar 

  19. 19.

    Căarbunar, B., Grama, A., Vitek, J., & Căarbunar, O. (2006). Redundancy and coverage detection in sensor networks. ACM Transactions on Sensor Networks, 2(1), 94–128.

    Article  Google Scholar 

  20. 20.

    Bejar Haro, B., Zazo, S., & Palomar, D. (2014). Energy efficient collaborative beamforming in wireless sensor networks. IEEE Transactions on Signal Processing, 62(2), 496–510.

    MathSciNet  Article  Google Scholar 

  21. 21.

    Cassandras, C., Wang, T., & Pourazarm, S. (2014). Optimal routing and energy allocation for lifetime maximization of wireless sensor networks with nonideal batteries. IEEE Transactions on Control of Network Systems, 1(1), 86–98.

    MathSciNet  Article  Google Scholar 

  22. 22.

    Najimi, M., Ebrahimzadeh, A., Andargoli, S., & Fallahi, A. (2014). Lifetime maximization in cognitive sensor networks based on the node selection. IEEE Sensors Journal, 14(7), 2376–2383.

    Article  Google Scholar 

  23. 23.

    Tian, D., & Georganas, N. D. (2002). A coverage-preserving node scheduling scheme for large wireless sensor networks. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (WSNA) (pp. 32–41). Atlanta, GA, USA.

  24. 24.

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

    Article  Google Scholar 

  25. 25.

    Handy, M., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster head selection. In Proceedings of IEEE international conference on mobile and wireless communications networks (MWCN) (pp. 368–372), Stockholm, Sweden.

  26. 26.

    Zhuo, W. (2011). Energy efficient clustering algorithm based on neighbours for wireless sensor networks. Journal of Shanghai Jiaotong University, 15(2), 150–153.

    MathSciNet  Article  Google Scholar 

  27. 27.

    Wang, S., & Chen, Z. (2013). LCM: A link-aware clustering mechanism for energy-efficient routing in wireless sensor networks. IEEE Sensors Journal, 13(2), 728–736.

    Article  Google Scholar 

  28. 28.

    Ayaz, B., Allen, A., & Wiercigroch, M. (2017). Improving routing performance of underwater wireless sensor networks. In IEEE InOCEANS 2017-Aberdeen (pp. 1–9).

  29. 29.

    Jaradat, Y., Masoud, M., & Jannoud, I. (2018). A mathematical framework of optimal number of clusters in 3d noise-prone wsn environment. IEEE Sensors Journal, 19(6), 2378–2388.

    Article  Google Scholar 

  30. 30.

    Azharuddin, M., & Jana, P. K. (2016). Particle swarm optimization for maximizing lifetime of wireless sensor networks. Computers and Electrical Engineering, 51, 26–42.

    Article  Google Scholar 

  31. 31.

    Zhong, J., Huang, Z., Feng, L., Du, W., & Li, Y. (2019). A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink. IEEE/CAA Journal of Automatica Sinica, 7(1), 223–236.

    Article  Google Scholar 

  32. 32.

    Zhu, X., Li, J., & Zhou, M. (2018). Optimal deployment of energy-harvesting directional sensor networks for target coverage. IEEE Systems Journal, 13(1), 377–388.

    Article  Google Scholar 

  33. 33.

    Zhu, X., Li, J., Chen, X., & Zhou, M. (2017). Minimum cost deployment of heterogeneous directional sensor networks for differentiated target coverage. IEEE Sensors Journal, 17(15), 4938–4952.

    Article  Google Scholar 

  34. 34.

    Tam, N. T., & Hai, D. T. (2018). Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Networks, 24(5), 1477–1490.

    Article  Google Scholar 

  35. 35.

    Rojas, D., & Barrett, J. (2019). Link quality evaluation of a wireless sensor network in metal marine environments. Wireless Networks, 25(3), 1253–1271.

    Article  Google Scholar 

  36. 36.

    Koutsopoulos, I., & Stanczak, S. (2012). The impact of transmit rate control on energy-efficient estimation in wireless sensor networks. IEEE Transactions on Wireless Communications, 11(9), 3261–3271.

    Article  Google Scholar 

  37. 37.

    Hsu, C.-C., Kuo, M.-S., Wang, S.-C., & Chou, C.-F. (2014). Joint design of asynchronous sleep-wake scheduling and opportunistic routing in wireless sensor networks. IEEE Transactions on Computers, 63(7), 1840–1846.

    MathSciNet  Article  Google Scholar 

  38. 38.

    De-Witt, J., & Shi, H. (2014). Maximizing lifetime for k-barrier coverage in energy harvesting wireless sensor networks. In IEEE global communications conference (GLOBECOM’14), Austin, TX (pp. 300–304).

  39. 39.

    Liu, W., Zhou, X., Durrani, S., Mehrpouyan, H., & Blostein, S. D. (2016). Energy harvesting wireless sensor networks: Delay analysis considering energy costs of sensing and transmission. IEEE Transactions on Wireless Communications, 15(7), 4635–4650.

    Google Scholar 

  40. 40.

    Yun, Y.-S., Xia, Y., Behdani, B., & Smith, J. (2013). Distributed algorithm for lifetime maximization in a delay-tolerant wireless sensor network with a mobile sink. IEEE Transactions on Mobile Computing, 12(10), 1920–1930.

    Article  Google Scholar 

  41. 41.

    Tashtarian, F., Hossein Yaghmaee Moghaddam, M., Sohraby, K., & Effati, S. (2015). On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks. IEEE Transactions on Vehicular Technology, 64(7), 3177–3189.

    Google Scholar 

  42. 42.

    Alrabea, A., Alzubi, O., & Alzubi, J. (2020). An enhanced mac protocol design prolong sensor network lifetime. International Journal on Communications Antenna and Propagation, 10(1), 37–43.

    Google Scholar 

  43. 43.

    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–70.

    Article  Google Scholar 

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Acknowledgements

This research was supported by Al-Zaytoonah Universityof Jordan fund. Project Number 06/12/2019-2020.

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Correspondence to Yousef Jaradat.

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Jaradat, Y., Masoud, M., Al-Jazzar, S. et al. Optimal network dimensions for energy conservation in clustered 3D WSN. Wireless Netw (2021). https://doi.org/10.1007/s11276-020-02527-5

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Keywords

  • Wireless sensor networks
  • Clustering protocols
  • Energy efficiency
  • 3D networks
  • Cube and cylinder networks
  • Optimization