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Wireless Networks

, Volume 24, Issue 5, pp 1793–1807 | Cite as

Virtual region based data gathering method with mobile sink for sensor networks

  • Chao Sha
  • Jian-mei Qiu
  • Tian-yu Lu
  • Ting-ting Wang
  • Ru-chuan Wang
Article
  • 131 Downloads

Abstract

To solve the hotspot problem in wireless sensor networks, a type of virtual region based data gathering method (VRDG) with one mobile sink is proposed. Network is divided into several virtual regions consisting of three or less data gathering unit. One or more leaders are selected in each region according to their residual energy as well as the distance to all of the neighbors. Only the leaders upload data to sink in data gathering phase that effectively reduce energy consumption and end-to-end delay. Moreover, the “maximum step distance” could be calculated out by nodes to find out the best transmission path to the leader which further balance energy consumption of the whole network. Simulation results show that VRDG is energy efficient in comparing with MSE, SEP and LEACH. It also does well in prolonging network lifetime as well as in enhancing the efficiency of data collection.

Keywords

Sensor networks Data gathering Mobile sink Virtual region Balance of energy consumption 

Notes

Acknowledgements

The subject is sponsored by the National Natural Science Foundation of P. R. China (61572260, 61672297), Jiangsu Natural Science Foundation for Excellent Young Scholar (BK20160089), Jiangsu Provincial Research Scheme of Natural Science for Higher Education Institutions (14KJB520029), Open Project of Provincial Key Laboratory for Computer Information Processing Technology of Soochow University (KJS1327), Open Project of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201517), A Project Funded by the Priority Academic Program Development of Jiangsu Higer Education Institutions(PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) and Innovation Project for Postgraduate of Jiangsu Province (SJZZ16_0147, SJZZ16_0149, SJZZ16_015, KYLX15_0842).

Author Contributions

Chao Sha proposed the main ideas of the VRDG algorithm while Jian-mei Qiu designed and conducted the simulations of the protocol. Tian-yu Lu and Ting-ting Wang analyzed the data, results and verified the theory. Ru-chuan Wang served as advisor to the above authors and gave suggestions on simulations, performance evaluation and writing. The manuscript write up was a combined effort from the five authors.

References

  1. 1.
    Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal, 15(8), 4576–4586.CrossRefGoogle Scholar
  2. 2.
    Mammu, A. S. K., Hernandez-Jayo, U., Sainz, N., & de la Iglesia, I. (2015). Cross-layer cluster-based energy-efficient protocol for wireless sensor networks. Sensors, 15(4), 8314–8336.CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.CrossRefGoogle Scholar
  4. 4.
    de Freitas, E. P., Heimfarth, T., Vinel, A., Wagner, F. R., Pereira, C. E., & Larsson, T. (2013). Cooperation among wirelessly connected static and mobile sensor nodes for surveillance applications. Sensors, 13(10), 12903–12928.CrossRefGoogle Scholar
  5. 5.
    Li, X., & Hunter, D. K. (2012). Four-dimensional Markov chain model of single-hop data aggregation with IEEE 802.15.4 in wireless sensor networks. Wireless Networks, 18(5), 469–479.CrossRefGoogle Scholar
  6. 6.
    Xie, S., & Wang, Y. (2014). Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wireless Personal Communications, 78(1), 231–246.CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Sun, X., & Wang, B. (2016). Efficient algorithm for K-barrier coverage based on integer linear programming. China Communications, 13(7), 16–23.CrossRefGoogle Scholar
  8. 8.
    Shen, J., Tan, H., Wang, J., Wang, J., & Lee, S. Y. (2015). A novel routing protocol providing good transmission reliability in underwater sensor networks. Journal of Internet Technology, 16(1), 171–178.Google Scholar
  9. 9.
    Al Nahas, H., Deogun, J. S., & Manley, E. D. (2009). Proactive mitigation of impact of wormholes and sinkholes on routing security in energy-efficient wireless sensor networks. Wireless Networks, 15(4), 431–441.CrossRefGoogle Scholar
  10. 10.
    Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Proceedings of the international workshop on sensor and actor network protocols and applications, SANPA 2004, Boston, MT, USA, August 22, 1–11.Google Scholar
  11. 11.
    Wang, J., Zhang, Z., & Xia, F. (2013). An energy efficient stable election-based routing algorithm for wireless sensor networks. Sensors, 13, 14301–14320.CrossRefGoogle Scholar
  12. 12.
    Gao, S., Zhang, H., & Das, S. K. (2011). Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Transactions on Mobile Computing, 10(4), 592–608.CrossRefGoogle Scholar
  13. 13.
    Ren, X., & Liang, W. (2013). The use of a mobile sink for quality data collection in energy harvesting sensor networks. In Proceedings of IEEE wireless communications and networks conference, WCNC2013, Shanghai, China, April 7–10, 1145–1150.Google Scholar
  14. 14.
    Shah, R. C., Roy, S., Jain, S., & Brun, W. (2003). Data MULEs: Modeling a three-tier architecture for sparse sensor networks. In Proceedings of IEEE international workshop on sensor network protocols and applications, Anchorage, AK, USA, 30–41.Google Scholar
  15. 15.
    Di Francesco, M., Das, S. K., & Anastasi, G. (2011). Data collection in wireless sensor networks with mobile elements: A survey. ACM Transactions on Sensor Networks, 8(1), 72–102.CrossRefGoogle Scholar
  16. 16.
    Somasundara, A. A., Kansal, A., Jea, D. D., Estrin, D., & Srivastava, M. B. (2006). Controllably mobile infrastructure for low energy embedded networks. IEEE Transactions on Mobile Computing, 5(8), 958–973.CrossRefGoogle Scholar
  17. 17.
    Ren, X., & Liang, W. (2012). Delay-tolerant data gathering in energy harvesting sensor networks with a mobile sink. In Proceedings of IEEE global communication conference, GLOBECOM 2012, Anaheim, California, USA, December 3–7, 93–99.Google Scholar
  18. 18.
    Yang, Y., Fonoage, M. I., & Cardei, M. (2010). Improving network lifetime with mobile wireless sensor networks. Computer Communications, 33(4), 409–419.CrossRefGoogle Scholar
  19. 19.
    Chakrabarti, A., Sabharwal, A., & Aazhang, B. (2006). Communication power optimization in a sensor network with a path-constrained mobile observer. ACM Transactions on Sensor Networks, 2(3), 219–240.CrossRefGoogle Scholar
  20. 20.
    Heinzelman, W. R., Chandrakasan, A. P., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless sensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences, Hawaii, HI, USA, January 4–7, 1–10.Google Scholar
  21. 21.
    Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. IEEE computer communication societies-INFOCOM 2003, San Francisco, USA, March 30–April 3, 1713–1723.Google Scholar
  22. 22.
    Wang, J., & Zhang, Z. Q. (2013). An improved stable election based routing protocol with mobile sink for wireless sensor networks. In Proceedings of the IEEE international conference on green computing and communications, GreenCom 2013, Beijing, China, August 20–23, 945–950.Google Scholar
  23. 23.
    He, L., Pan, J., & Xu, J. (2013). A progressive approach to reducing data collection latency in wireless sensor networks with mobile elements. IEEE Transactions on Mobile Computing, 12(7), 1308–1320.CrossRefGoogle Scholar
  24. 24.
    Yun, Y., & Xia, Y. (2010). Maximizing the Lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transactions on Mobile Computing, 9(9), 1920–1930.Google Scholar
  25. 25.
    Chen, J., Salim, M. B., & Matsumoto, M. (2010). Modeling the energy performance of event-driven wireless sensor network by using static sink and mobile sink. Sensors, 10(12), 10876–10895.CrossRefGoogle Scholar
  26. 26.
    Mehrabi, A., & Kim, K. (2015). Maximizing data collection throughput on a path in energy harvesting sensor networks using a mobile sink. IEEE Transactions on Mobile Computing, 14(8), 1–16.Google Scholar
  27. 27.
    Khan, M., Gansterer, W., & Haring, G. (2013). Static versus mobile sink: The influence of basic parameters on energy efficiency in wireless sensor networks. Computer Communications, 36(9), 965–978.CrossRefGoogle Scholar
  28. 28.
    Shi, Y., & Hou, Y. T. (2008). Theoretical results on base station movement problem for sensor network. In Proceedings of IEEE international conference on information and communication, INFOCOM 2008, Phoenix, AZ, USA, April 15–17, 376–384.Google Scholar
  29. 29.
    Jain, S., Shah, R. C., Brunette, W., Borriello, G., & Roy, S. (2006). Exploiting mobility for energy efficient data collection in sensor networks. Mobile Networks and Applications, 11(3), 327–339.CrossRefGoogle Scholar
  30. 30.
    Xing, G., Wang, T., Xie, Z., & Jia, W. (2008). Rendezvous design algorithms for wireless sensor networks with a mobile base station. Proceedings of ACM MobiHoc 2008, Hong Kong SAR, China, May 27–30, 231–240.Google Scholar
  31. 31.
    Somasundara, A., Kansal, A., Jea, D., Estrin, D., & Srivastava, M. (2006). Controllably mobile infrastructure for low energy embedded networks. IEEE Transactions on Mobile Computing, 5(8), 958–973.CrossRefGoogle Scholar
  32. 32.
    Lin, L., Shroff, N. B., & Stikant, R. (2007). Asymptotically optimal energy-aware routing for multi-hop wireless networks with renewable energy sources. IEEE/ACM Transactions on Networking, 15(5), 1021–1034.CrossRefGoogle Scholar
  33. 33.
    Luo, J., Panchard, J., Piorkowski, M., Grossglauser, M., & Hubaux, J. (2006). MobiRoute: Routing towards a mobile sink for improving lifetime in sensor networks. Proceedings of the international conference on distributed computing in sensor systems, DCOSS 2006, San Francisco, CA, USA, June 18–20, 480–497.Google Scholar
  34. 34.
    Somasundara, A., Ramamoorthy, A., & Srivastava, M. (2004). Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines. Proceedings of the 25th IEEE international real-time systems symposium, RTSS 2004, Lisbon, Portugal, December 5–8, 296–305.Google Scholar
  35. 35.
    Sha, C., Qiu, J. M., Li, S. Y., Qiang, M. Y., & Wang, R. C. (2016). A type of low-latency data gathering method with multi-sink for sensor networks. Sensors, 16(6), 1–29.CrossRefGoogle Scholar
  36. 36.
    Kimençe, S., & Bekmezci, I. (2014). Weighted relay node placement for wireless sensor network connectivity. Wireless Networks, 20(4), 553–562.CrossRefGoogle Scholar
  37. 37.
    Kumar, R., & Kumar, U. (2012). A hierarchal cluster framework for wireless sensor network. In Proceedings of the international conference on advances in computing and communications, ICACC 2012, Shanghai, China, December 29–30, 46–50.Google Scholar
  38. 38.
    Yusof, K. M., Woods, J., & Fitz, S. (2012). Short-range and near ground propagation model for wireless sensor networks. In Proceedings of the IEEE student conference on research and development, SCOReD 2012, Pulau Pinang, Malaysia, December 2012, 124–128.Google Scholar
  39. 39.
    Hua, C., & Yum, T. S. P. (2006). Maximum lifetime routing and data aggregation for wireless sensor networks. In Proceedings of the 5th international IFIP-TC6 networking conference, Coimbra, Portugal, May 15–19, 840–855.Google Scholar
  40. 40.
    Ardizzone, E., Cascia, M. L., Re, G. L., & Ortolani, M. (2005). An integrated architecture for surveillance and monitoring in an archaeological site. In J. K. Aggarwal, R. Cucchiara, E. Chang, & Y.-F. Wang (Eds.), VSSN’05, Singapore (pp. 79–86). New York: ACM Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Chao Sha
    • 1
    • 2
    • 3
    • 4
  • Jian-mei Qiu
    • 1
  • Tian-yu Lu
    • 1
  • Ting-ting Wang
    • 1
  • Ru-chuan Wang
    • 1
    • 2
    • 3
  1. 1.Nanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  3. 3.Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworksNanjingChina
  4. 4.Nanjing University of Information Science and TechnologyNanjingChina

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