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A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems

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Abstract

An internet of things system is expected to integrate the sensing, communication, networking, and cloud computing technologies in large-scale monitoring environments. The main monitoring infrastructure of internet of things systems is wireless sensor networks. Energy saving becomes one of the most important features for the sensing nodes to extend their lifetime in such systems. To provide reasonable energy consumption and to improve the network lifetime for the internet of things systems, efficient energy saving schemes must be developed. In this paper, a distributed cluster computing energy-efficient routing scheme is proposed to reduce the energy consumption and to extend the network lifetime for the internet of things systems. The main goal of this scheme is to reduce the data transmission distances of sensing nodes by using the cluster structure concepts. A center of gravity among the sensing nodes is calculated and the residual energy of each sensing node is taken into account in the cluster for selecting a suitable cluster head node. Based on the suitable cluster architecture, the data transmission distances between the sensing nodes can be reduced. The energy consumption is reduced and the lifetime is extended for the sensing nodes by balancing the network load. Simulation results show that the proposed scheme outperforms the previously known schemes in terms of the energy consumption and network lifetime for the internet of things systems.

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References

  1. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54, 2787–2805.

    Article  MATH  Google Scholar 

  2. Bandyopadhyay, D., & Sen, J. (2011). Internet of things: Applications and challenges in technology and standardization. Wireless Personal Communications, 58, 49–69.

    Article  Google Scholar 

  3. Guo, B., Zhang, D., Wang, Z., Yu, Z., & Zhou, X. (2013). Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things. Journal of Network and Computer Applications, 36, 1531–1539.

    Article  Google Scholar 

  4. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645–1660.

    Article  Google Scholar 

  5. Corke, P., Wark, T., Jurdak, R., Hu, W., Valencia, P., & Moore, D. (2010). Environmental wireless sensor networks. Proceedings of the IEEE, 98(11), 1903–1917.

    Article  Google Scholar 

  6. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  7. Tubaishat, M., & Madria, S. (2003). Sensor networks: An overview. IEEE Potentials, 22(2), 20–23.

    Article  Google Scholar 

  8. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.

    Article  Google Scholar 

  9. Chamam, A., & Pierre, S. (2009). On the planning of wireless sensor networks: Energy-efficient clustering under the joint routing and coverage constraint. IEEE Transactions on Mobile Computing, 8(8), 1078–1086.

    Article  Google Scholar 

  10. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (pp. 1–10).

  11. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of fourth IEEE conference on mobile and wireless communications network (pp. 368–372).

  12. Babaie, S., Zadeh, A. K., & Amiri, M. G. (2010). The new clustering algorithm with cluster members bounds for energy dissipation avoidance in wireless sensor network. In Proceedings of computer design and applications (ICCDA) (pp. 613–617).

  13. Heinzelman, W. B., Chandrakasan, P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  14. Muruganathan, S. D., Ma, D. C. F., Bhasin, R. I., & Fapojuwo, A. O. (2005). A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 43(3), S8–S13.

    Article  Google Scholar 

  15. Bajaber, F., & Awan, I. (2009). Centralized dynamic clustering for wireless sensor network. In Proceedings of international conference on advanced information networking and applications workshops (pp. 193–198).

  16. Xuegong, Q., & Yan, C. (2010). A control algorithm based on double cluster-head for heterogeneous wireless sensor network. In Proceedings of industrial and information systems (IIS) (pp. 541–544).

  17. Yun, Y.-U., Choi, J.-K., Hao, N., & Yoo, S.-J. (2010). Location-based spiral clustering for transmission scheduling in wireless sensor networks. In Proceedings of advanced communication technology (ICACT) (pp. 470–475).

  18. Chang, J.-Y., & Ju, P.-H. (2012). An efficient cluster-based power saving scheme for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking. doi:10.1186/1687-1499-2012-172

  19. Tarigh, H. D., & Sabaei, M. (2011). A new clustering method to prolong the lifetime of WSN. In Proceedings of international conference on computer research and development (ICCRD) (pp. 143–148).

  20. Lindsey, S., Raghavendra, C., & Sivalingam, K. M. (2002). Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 13(9), 924–935.

    Article  Google Scholar 

  21. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings of IEEE aerospace conference, vol. 3 (pp. 1125–1130).

  22. Kemei, J. W., & Zhou, D. (2003). Chain-based protocols for data broadcasting and gathering in the sensor networks. In Proceedings of international parallel and distributed processing symposium (pp. 22–26).

  23. Majumder, K., Ray, S., & Sarkar, S. K. (2010). A novel energy efficient chain based hierarchical routing protocol for wireless sensor networks. In Proceedings of international conference on emerging trends in robotics and communication technologies (pp. 339–344).

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Chang, JY. A Distributed Cluster Computing Energy-Efficient Routing Scheme for Internet of Things Systems. Wireless Pers Commun 82, 757–776 (2015). https://doi.org/10.1007/s11277-014-2251-8

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  • DOI: https://doi.org/10.1007/s11277-014-2251-8

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