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Data Aggregation Routing for Rechargeable Wireless Sensor Networks in Forest Monitoring

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Abstract

In rechargeable wireless sensor networks (r-WSNs), higher data transmitted efficiency is required because sensor have to operate in a very low duty cycle owing to sporadic availability of energy. In r-WSNs, Data collected by many sensors is based on common phenomena, and hence there is a high probability that this data has some redundancy. In this work, we address the problem of jointly optimizing data aggregation and routing so that the network workload can be maximized. Establish the relationship model between data aggregation rate and throughput, so that the balanced was set up between the data aggregation rate and maximum network data traffic. Through the use of optimal candidate sample allocation, the algorithm can coverage efficiently and can make the maximum data aggregation rate flow to the network while maximizing network workload. Simulations are carried out to show that the proposed algorithm can significantly improve workload.

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References

  1. Shi, E., & Perrig, A. (2004). Designing secure sensor networks. IEEE Wireless Communications, 11(6), 38–43.

    Article  Google Scholar 

  2. 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 

  3. Heinzelman, W. B., Murphy, A. L., Carvalho, H. S., & Perillo, M. A. (2004). Middleware to support sensor network applications. IEEE Network, 18(1), 6–14.

    Article  Google Scholar 

  4. Herring, C., & Kaplan, S. (2000, October). Component-based software systems for smart environments. In: IEEE Personal Communications (pp. 60–61).

  5. Starner, T. (1996). Human-powered wearable computing. IBM Systems Journal, 35(3–4), 618–629.

    Article  Google Scholar 

  6. Shenck, N., & Paradiso, J. (2001). Energy scavenging with shoe-mounted piezoelectrics. IEEE Micro, 21(3), 30–42.

    Article  Google Scholar 

  7. Kymissis, J., Kendall, C., Paradiso, J., & Gershenfeld, N. (1998, October). Parasitic power harvesting in shoes. In Second international symposium on wearable computers (pp. 132–139).

  8. Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. In ACM transactions on embedded computing systems (Vol. 6, p. 4).

  9. Chalasani, S., & Conrad, J. (2008, April). A survey of energy harvesting sources for embedded systems. In IEEE Southeastcon (pp. 442–447).

  10. Rabindra, B., Yong-Ki, K., & Jae-Woo, C. (2009). A new approach for energy-balanced data aggregation in wireless sensor networks. In IEEE ninth international conference on computer and information technology (pp. 9–15).

  11. Kasirajan, P., Larsen, C., & Jagannathan, S. A. (2010). A new adaptive compression scheme for data aggregation in wireless sensor networks. In IEEE wireless communications and networking conference WCNC 2010, Sydney: NSW, Australia.

  12. Koutsopoulos, I., & Halkidi, M. (2010, May). Measurement aggregation and routing techniques for energy-efficient estimation in wireless sensor networks. In 8th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks, WiOpt 2010. Avignon, France.

  13. Jeongt, J., Kim, J. , Cha, W., et al. (2010) A QoS-aware data aggregation in wireless sensor networks[C]. In 12th international conference on advanced communication technology: ICT for green growth and sustainable development, ICACT 2010-proceedings, Korea (pp. 156–161).

  14. Ozdemir, S., & Cam, H. (2010). Integration of false data detection with data aggregation and confidential transmission in wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 7, 736–749.

    Article  Google Scholar 

  15. Shoudong, Z., Nikolaidis, I., & Harms, J. J. (2006). Extending sensor network lifetime via first hop data aggregation[C]. In 5th IEEE international performance, computing, and communications conference, Phoenix, AZ, United States, IPCCC (pp. 397–405).

  16. Ghaffariyan, P. (2010). An effective data aggregation mechanism for wireless sensor networks. In 6th international conference on wireless communications, networking and mobile computing, WiCOM 2010, Chengdu, China.

  17. Leandro, A., Daniel, L., & Regina, B. A. (2010). Scalable and dynamic data aggregation aware routing protocol for wireless sensor networks[C]. 13th ACM international conference on modeling, analysis and simulation of wireless and mobile systems, MSWiM 2010, Bodrum, Turkey, pp. 110–117.

  18. Hua, C., & Peter Yum, T.-S. (2008). Data aggregated maximum lifetime routing for wireless sensor networks. Ad Hoc Networks, 6(3), 380–392.

    Article  Google Scholar 

  19. Sudevalayam, S., & Kulkarni, P. (2011). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys Tutorials, 13(3), 443–461.

    Article  Google Scholar 

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

  21. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the sixth ACM international conference on mobile computing and networking New York: ACM Press (August 2000).

  22. Chu H.-C., Siao W.-T., Wu W.-T. (2011). Design and implementation an energy-aware routing mechanism for solar wireless sensor networks. In IEEE 13th international conference on high performance computing and communications (HPCC), 2–4 September 2011 (pp. 881–886).

  23. Hyuntaek Kwon, Donggeon Noh, Junu Kim et al. Low-Latency Routing for Energy-Harvesting Sensor Networks, 4th International Conference, UIC 2007, Hong Kong, China, July 11–13, 2007. Proceedings, pp 422–433.

  24. Noh, D., Yoon, I., & Shin, H. (2008). Low-latency geographic routing for asynchronous energy-harvesting WSNs. Journal of Networks, 3(1), 78–85.

    Article  Google Scholar 

  25. Zhang, J., Shen, X., & Tang, S. (2011). Energy efficient joint data aggregation and link scheduling in solar sensor networks. Computer Communications, 34(18), 2217–2226.

    Article  Google Scholar 

  26. Seah, W. K. G., Eu, Z.-A., Tan, H.-P. (2009) Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)—survey and challenges. In 1st international conference on wireless communication, vehicular technology, information theory and aerospace electronic systems technology, 2009. Wireless VITAE 2009. (pp. 1–5).

  27. Satapathy, S. S., & Sarma, N. (2006, April) TREEPSI: Tree based energy efficient protocol for sensor information. In Wireless and optical communications networks 2006 IFIP international conference.

  28. von Rickenbach, P., & Wattenhofer, R. (2004). Gathering correlated data in sensor networks, In DIALM-POMC04: Proceedings of the 2004 joint workshop on foundations of mobile computing (pp. 60–66) New York, NY, USA: ACM Press.

  29. Cristescu, R., Beferull-Lozano, B., & Vetterli, M. (2004). On network correlated data gathering, In Infocom04, Hong Kong.

  30. Kuhn, F., Wattenhofer, R., & Zollinger, A. (2002). Asymptotically optimal geometric mobile ad-hoc routing. In Proceedings of DIALM99 (pp. 24–33).

  31. Bose, P., Morin, P., Stojmenovi, I., & Urrutia, J. (1999). Routing with guaranteed delivery in ad hoc wireless networks. In Proceedings of DIALM99 (pp. 48–55).

  32. Karp, B., & Kung, H. (2000). GPSR: Greedy perimeter stateless routing for wireless networks, In MobiCom00, 2000 (pp. 243–254).

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Acknowledgments

This work was supported in part by The Six Talent Peaks of Jiangsu Province under Grant DZXX-149-148 and The Forestry SanXin Project of Jiangsu Province under Grant lysx[2014]07. We also received A Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

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  1. College of Information Science and Technology, Nanjing Forestry University, Nanjing, China

    Demin Gao, Yunfei Liu, Fuquan Zhang & Jun Song

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  1. Demin Gao
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  2. Yunfei Liu
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  3. Fuquan Zhang
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  4. Jun Song
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Correspondence to Demin Gao.

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Gao, D., Liu, Y., Zhang, F. et al. Data Aggregation Routing for Rechargeable Wireless Sensor Networks in Forest Monitoring. Wireless Pers Commun 79, 773–788 (2014). https://doi.org/10.1007/s11277-014-1886-9

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  • DOI: https://doi.org/10.1007/s11277-014-1886-9

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