Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Efficient Energy Utilization Through Optimum Number of Sensor Node Distribution in Engineered Corona-Based (ONSD-EC) Wireless Sensor Network

Abstract

An optimum sensor node deployment in wireless sensor network can sense the event precisely in many real time scenarios for example forests, habitat, battlefields, and precision agriculture. Due to these applications, it is necessary to distribute the sensor node in an efficient way to monitor the event precisely and to utilize maximum energy during network lifetime. In this paper, we consider the energy hole formation due to the unbalanced energy consumption in many-to-one wireless sensor network. We propose a novel method using the optimum number of sensor node Distribution in Engineered Corona-based wireless sensor network, in which the interested area is divided into a number of coronas. A mathematical models is proposed to find out the energy consumption rate and to distribute the optimum number of sensor node in each corona according to energy consumption rate. An algorithm is proposed to distribute the optimum number of sensor nodes in corona-based networks. Simulation result shows that the proposed technique utilized 95 % of the total energy of the network during network lifetime. The proposed technique also maximizes the network lifetime, data delivery and reduce the residual energy ratio during network lifetime.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Jian, L., & Mohapatra P. (2005). An analytical model for the energy hole problem in many-to-one sensor networks. In IEEE 62nd Vehicular Technology Conference, VTC-2005-Fall (vol. 4, pp. 2721–2725).

  2. 2.

    Song, C., Cao, J., Liu, M., Zheng, Y., Gong, H., & Chen, G. (2008). Mitigating energy holes based on transmission range adjustment in wireless sensor networks. In Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (pp. 1–7), Hong Kong.

  3. 3.

    Rahman, A. U., Hasbullah, H., & Sama, N. U. (2012). Impact of Gaussian deployment strategies on the performance of wireless sensor network. In 2012 International Conference on Computer & Information Science (ICCIS) (vol. 2, pp. 771–776).

  4. 4.

    Lian, J., Naik, K., & Agnew, G. B. (2006). Data capacity improvement of wireless sensor networks using non-uniform sensor distribution. International Journal of Distributed Sensor Networks, 2, 121–145.

  5. 5.

    Tang, J., Hao, B., & Sen, A. (2006). Relay node placement in large scale wireless sensor networks. Computer Communications, 29, 490–501.

  6. 6.

    Tilak, S., Abu-Ghazaleh, N. B., & Heinzelman, W. (2002). Infrastructure tradeoffs for sensor networks. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (pp. 49–58). Georgia, USA: Atlanta.

  7. 7.

    Ishizuka, M., & Aida, M. (2004). Performance study of node placement in sensor networks. In 24th International Conference on Distributed Computing Systems Workshops (pp. 598–603).

  8. 8.

    Krishnamurthy, L., Adler, R., Buonadonna, P., Chhabra, J., Flanigan, M., Kushalnagar, N., et al. (2005). Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (pp. 64–75). California, USA: San Diego.

  9. 9.

    Brooks, A., Makarenko, A., Kaupp, T., Williams, S., Durrant-Whyte, H., et al. (2006). Implementation of an indoor active sensor network. In M. Ang & O. Khatib (Eds.), Experimental Robotics IX (Vol. 21, pp. 397–406). Berlin: Springer.

  10. 10.

    Petrushin, V. A., Gang, W., Shakil, O., Roqueiro, D., & Gershman, V. (2006). Multiple-sensor indoor surveillance system. In The 3rd Canadian Conference on Computer and Robot Vision (pp. 40–46).

  11. 11.

    Paek, J., Chintalapudi, K., Caffrey, J., Govindan, R., & Masri, S. (Eds.) (2005). A wireless sensor network for structural health monitoring: Performance and experience. UC Los Angeles: Center for Embedded Network Sensing.

  12. 12.

    Mechitov, K., Kim, W., Agha, G., & Nagayama, T. (Eds.) (2004). High-frequency distributed sensing for structure monitoring. In Proceedings of the First International on Networked Sensing Systems (INSS 04)

  13. 13.

    Berry, J., Fleischer, L., Hart, W., & Phillips, C. Watson, J. (2005). Sensor placement in municipal water networks. Journal of Water Resources Planning Management 131(3), 237–243.

  14. 14.

    Watson, J. P., Greenberg, H. J., & Hart, W. E. (2004). A multiple-objective analysis of sensor placement optimization in water networks. In Proceedings of the World Water and Environment Resources Conference.

  15. 15.

    Dhillon, S. S., & Chakrabarty, K. (2003). Sensor placement for effective coverage and surveillance in distributed sensor networks. In IEEE Wireless Communications and Networking (WCNC) (vol. 3, pp. 1609–1614).

  16. 16.

    Flathagen, J., Kure, Ø., Engelstad, P. E. (2011). Constrained-based multiple sink placement for wireless sensor networks. In IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS) (pp. 783–788).

  17. 17.

    De, D., Sen, A., & Gupta, M. D. (2012). Cluster based energy efficient lifetime improvement mechanism for WSN with multiple mobile sink and single static sink. In Third International Conference on Computer and Communication Technology (ICCCT) (pp. 197–199).

  18. 18.

    Jia, J., Chen, J., Wang, X., & Zhao, L. (2012). Energy-balanced density control to avoid energy hole for wireless sensor networks. International Journal of Distributed Sensor Networks.

  19. 19.

    Mahani, A., Kargar, A., Kavian, Y. S., & Rashvand, H. F. (2012). Non-uniform distribution of multi-hop sensor networks: Performance improvement and energy hole mitigation. IET Wireless Sensor Systems, 2, 302–308.

  20. 20.

    Zhiqiang, P., & Changqing, X. (2011). A max-energy-utilization deployment scheme in wireless sensor networks. In IEEE 14th International Conference on Computational Science and Engineering (CSE) (pp. 413–420).

  21. 21.

    Xenakis, A., Katsavounidis, I., & Stamoulis, G. (2012). Investigating Wireless Sensor Network lifetime under static routing with unequal energy distribution. In Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC). 2012 Asia-Pacific (pp. 1–7).

  22. 22.

    Ramos, H. S., Oliveira, E. M. R., Boukerche, A., Frery, A. C., & Loureiro, A. A. F. (2012). Characterization and mitigation of the energy hole problem of many-to-one communication in Wireless Sensor Networks. In International Conference on Computing, Networking and Communications (ICNC) (pp. 954–958).

  23. 23.

    Azim, M. M. A. (2009). MAP: Energy Efficient Routing Protocol for Wireless Sensor Networks. In Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, ICUT ’09 (pp. 1–6).

  24. 24.

    Zhang, G., Liu, G., Chen, W., & Yang, C. (2012). Quantitative analysis of cluster-head selection for wireless sensor networks. In World Automation Congress (WAC) (pp. 277–281).

  25. 25.

    Min-Gon, K., Young-Tae, H., & Hong-Shik, P. (2011). Energy-aware hybrid data aggregation mechanism considering the energy hole problem in asynchronous MAC-based WSNs. IEEE Communications Letters, 15, 1169–1171.

  26. 26.

    Wang, D., Xie, B., & Agrawal, D. P. (2008). Coverage and lifetime optimization of wireless sensor networks with Gaussian distribution. IEEE Transactions on Mobile Computing, 7, 1444–1458.

  27. 27.

    Liang, W., Xu, J., & Liu, Y. (2011). Towards energy saving and load balancing data aggregation for wireless sensor network. Information Technoogy Journal, 10, 409–415.

  28. 28.

    Zhiming L., & Lin, L. (2009). Sensor node deployment in wireless sensor networks based on improved particle swarm optimization. In International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD) (pp. 215–217).

  29. 29.

    Pustchi, N., & Korkmaz, T. (2012). Improving packet reception rate for mobile sinks in wireless sensor networks. In IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) (pp. 1–9).

  30. 30.

    Nazir, B., & Hasbullah, H. (2010). Mobile sink based routing protocol (MSRP) for prolonging network lifetime in clustered wireless sensor network. In International Conference on Computer Applications and Industrial Electronics (ICCAIE) (pp. 624–629).

Download references

Author information

Correspondence to Atiq Ur Rahman.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Rahman, A.U., Hasbullah, H. & Sama, N.U. Efficient Energy Utilization Through Optimum Number of Sensor Node Distribution in Engineered Corona-Based (ONSD-EC) Wireless Sensor Network. Wireless Pers Commun 73, 1227–1243 (2013). https://doi.org/10.1007/s11277-013-1275-9

Download citation

Keywords

  • Corona-based wireless sensor network
  • Energy hole formation
  • Unbalanced energy consumption
  • Optimum number of sensor node distribution