iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11277-020-07824-y
One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks | Wireless Personal Communications Skip to main content
Springer Nature Link
Log in

One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. https://doi.org/10.1016/S1389-1286(01)00302-4.

    Article  Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communication Magazine, 40(8), 102–114. https://doi.org/10.1109/MCOM.2002.1024422.

    Article  Google Scholar 

  3. Johnson, D., Maltz, D., & Jetcheva, J. (2004). ‘The dynamic source routing (DSR) protocol for mobile ad hoc networks’. IETF Internet draft. draft-ietf-manet-dsr-10.txt.

  4. Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing’. In Proceedings of International Conference Mobile Computing Systems and Applications, LA, USA, February, pp. 90–100.

  5. Agarwal, A., Gupta, K., & Yadav, K. P. (2016) A novel energy efficiency protocol for WSN based on optimal chain routing. In Proceedings of International Conference Computing for Sustainable Global Development, New Delhi, India, March 2016, pp. 368–373.

  6. Ang, K. L.-M., Seng, J. K. P., & Zungeru, A. M. (2017). Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. IEEE Systems Journal, 12(1), 1–11. https://doi.org/10.1109/jsyst.2016.2630691.

    Article  Google Scholar 

  7. Kaur, N., & Sood, S. K. (2017). An energy-efficient architecture for the Internet of Things. IEEE Systems Journal, 11(2), 796–805. https://doi.org/10.1109/JSYST.2015.2469676.

    Article  Google Scholar 

  8. Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy efficient direction-based PDORP routing protocol for WSN. IEEE Access, 4, 3182–3194. https://doi.org/10.1109/ACCESS.2016.2576475.

    Article  Google Scholar 

  9. Siavoshi, S., Kavian, Y. S., & Sharif, H. (2016). Load-balanced energy efficient clustering protocol for wireless sensor networks. IET Wireless Sensor Systems, 6(3), 67–73. https://doi.org/10.1049/iet-wss.2015.0069.

    Article  Google Scholar 

  10. Tseng, T.-C., & Hsieh, T.-Y. (2004). An architecture for power-saving communications in a wireless mobile ad hoc network based on location information. Microprocessors and Microsystems, 28(4), 457–465. https://doi.org/10.1016/j.micpro.2004.05.002.

    Article  Google Scholar 

  11. Tseng, T.-C., & Hsieh, T.-Y. (2002) Fully energy-aware and location-aware protocols for wireless multihop ad hoc networks. In Proceedings of International Conference Computer Communications and Networks, FL, USA, October 2002, pp. 608–613.

  12. Cayirpunar, O., Travli, B., Kadioglu-Urtis, E., & Uludag, S. (2017). Optimal mobility patterns of multiple base stations for wireless sensor network lifetime maximization. IEEE Sensors Journal, 17(21), 7177–7188. https://doi.org/10.1109/JSEN.2017.2747499.

    Article  Google Scholar 

  13. Mohemed, R. E., Saleh, A. I., Abdelrazzak, M., & Samra, A. S. (2017). Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Computer Networks, 114, 51–66. https://doi.org/10.1016/j.comnet.2016.12.011.

    Article  Google Scholar 

  14. Ramos, H. S., Boukerche, A., Oliveira, A. L. C., Frery, A. C., Oliveira, E. M. R., & Loureiro, A. A. F. (2016). On the deployment of large-scale wireless sensor networks considering the energy hole problem. Computer Networks, 110, 154–167. https://doi.org/10.1016/j.comnet.2016.09.013.

    Article  Google Scholar 

  15. Liu, X. (2016). A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. Journal of Network and Computer Applications, 67, 43–52. https://doi.org/10.1016/j.jnca.2016.02.018.

    Article  Google Scholar 

  16. Fu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223–237. https://doi.org/10.1016/j.future.2018.08.031.

    Article  Google Scholar 

  17. Fu, X., Yao, H., & Yang, Y. (2019). Cascading failures in wireless sensor networks with load redistribution of links and nodes. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2019.101900.

    Article  Google Scholar 

  18. Fu, X., Yao, H., & Yang, Y. (2019). Modeling and analyzing cascading dynamics of the clustered wireless sensor network. Reliability Engineering & System Safety, 186, 1–10. https://doi.org/10.1016/j.ress.2019.02.009.

    Article  Google Scholar 

  19. Kumar, S., Chaudhary, R., Deepak, A., & Dash, D (2016) An integer linear formulation scheme for data collection in wireless sensor network using mobile element. In Proceedings of International Conference Wireless and Optical Communications Networks, Hyderabad, India, July 2016, pp 1–6.

  20. Yan, F., Zhang, X., Tao, L., & Zhang, H. (2018). Network coding-based flooding with a mobile sink in low-duty-cycle wireless sensor networks. IEEE Transactions on Mobile Computing, 18(8), 1857–1869. https://doi.org/10.1109/TMC.2018.2868664. (To be appeared).

    Article  Google Scholar 

  21. Djedouboum, A. C., Ari, A. A. A., Gueroui, A. M., Mohamadou, A., & Aliouat, Z. (2018). Big data collection in large-scale wireless sensor networks. Sensors, 18(12), 1–34. https://doi.org/10.3390/s18124474.

    Article  Google Scholar 

  22. Jerew, O., & Al Bassam, N. (2019). Delay tolerance and energy saving in wireless sensor networks with a mobile base station. Wireless Communications and Mobile Computing, 2019, 1–12. https://doi.org/10.1155/2019/3929876.

    Article  Google Scholar 

  23. Zhang, L., & Wan, C. (2019). Dynamic path planning design for mobile sink with burst traffic in a region of WSN. Wireless Communications and Mobile Computing, 2019, 1–8. https://doi.org/10.1155/2019/2435712.

    Article  Google Scholar 

  24. Lu, Y., Kuonen, P., Hirsbrunner, B., & Lin, M. (2017). Benefits of data aggregation on energy consumption in wireless sensor networks. IET Communications, 11(8), 1216–1223. https://doi.org/10.1049/iet-com.2016.0990.

    Article  Google Scholar 

  25. Padmanaban, Y., & Muthukumarasamy, M. (2018). Energy-efficient clustering algorithm for structured wireless sensor networks. IET Networks, 7(4), 265–272. https://doi.org/10.1049/iet-net.2017.0112.

    Article  Google Scholar 

  26. Mehto, A., Tapaswi, S., & Pattanaik, K. K. (2019) Rendezvous point based delay-efficient trajectory formation for mobile sink in wireless sensor networks. In Proceedings of International Conference Computing, Communication and Networking Technologies, Kanpur, India, September 2019, pp. 1–6.

  27. Kaswan, A., Jana, P. K., & Azharuddin, M. (2017) A delay efficient path selection strategy for mobile sink in wireless sensor networks. In Proceedings International Conference Computing, Communications and Informatics, Udupi, India, September 2017, pp. 168–173.

  28. Liu, W., Xi, X., & Yang, M. (2017) Latency constrained trajectory planning in wireless sensor networks with mobile sink. In Proceedings International Conference Computer and Communications, Chengdu, China, Decomber 2017, pp. 352–356.

  29. Sundani, H., Li, H., Devabhaktuni, V., Alam, M., & Bhattacharya, P. (2011). Wireless sensor network simulators: A survey and comparisons. International Journal of Computer Networks, 2(5), 249–265.

    Google Scholar 

  30. Nayyar, A., & Singh, R. (2015). A comprehensive review of simulation tools for wireless sensor networks. Journal of Wireless Networking and Communications, 5(1), 19–47. https://doi.org/10.5923/j.jwnc.20150501.03.

    Article  Google Scholar 

  31. Chhimwal, P., Rai, D. S., & Rawat, D. (2013). Comparison between different wireless sensor simulation tools. IOSR Journal of Electronics and Communication Engineering, 5(2), 54–60. https://doi.org/10.9790/2834-0525460.

    Article  Google Scholar 

  32. Hung, T. C., Ngoc, D. T., The, P. T., Hieu, L. N., Huynh, L. N. T., & Tam, L. D. (2019) A moving direction proposal to save energy consumption for mobile sink in wireless sensor network. In Proceedings International Conference Advanced Communications Technology, Kwangwoon_Do, Korea, pp. 107–110.

Download references

Author information

Authors and Affiliations

  1. Interdisciplinary Program of Green and Information Technology, National Taitung University, Taitung, Taiwan, R.O.C.

    Yi-Chao Wu

Authors
  1. Yi-Chao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Chao Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, YC. One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks. Wireless Pers Commun 116, 2855–2872 (2021). https://doi.org/10.1007/s11277-020-07824-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07824-y

Keywords

Search

Navigation