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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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.
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.
Rodgers, M., Pai, V., & Conroy, R. (2015). Recent advances in wearable sensors for health monitoring. IEEE Sensors Journal, 15(6), 3119–26.
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.
Singh, S., Kumar, P., & Singh, J. (2017). A survey on successors of LEACH protocol. IEEE Access, 5, 4298–328.
Alrabea, A., Alzubi, O. A., & Alzubi, J. A. (2019). A task-based model for minimizing energy consumption in WSNs. Energy Systems, 1–18.
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.
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.
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).
Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston: Boston University Computer Science Department.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Afsar, M., & Tayarani-N, M.-H. (2014). Clustering in sensor networks: a literature survey. Journal of Network and Computer Applications, 46, 198–226.
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.
Zhuo, W. (2011). Energy efficient clustering algorithm based on neighbours for wireless sensor networks. Journal of Shanghai Jiaotong University, 15(2), 150–153.
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.
Ayaz, B., Allen, A., & Wiercigroch, M. (2017). Improving routing performance of underwater wireless sensor networks. In IEEE InOCEANS 2017-Aberdeen (pp. 1–9).
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.
Azharuddin, M., & Jana, P. K. (2016). Particle swarm optimization for maximizing lifetime of wireless sensor networks. Computers and Electrical Engineering, 51, 26–42.
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.
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.
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.
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.
Rojas, D., & Barrett, J. (2019). Link quality evaluation of a wireless sensor network in metal marine environments. Wireless Networks, 25(3), 1253–1271.
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.
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.
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).
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.
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.
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.
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.
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.
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This research was supported by Al-Zaytoonah Universityof Jordan fund. Project Number 06/12/2019-2020.
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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 27, 1821–1833 (2021). https://doi.org/10.1007/s11276-020-02527-5
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DOI: https://doi.org/10.1007/s11276-020-02527-5