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Physarum-inspired routing protocol for energy harvesting wireless sensor networks

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Abstract

In order to resolve the traditional limited lifetime problem, energy harvesting technology has been introduced into wireless sensor network (WSN) in recent years, engendering a new kind of network which is called energy harvesting wireless sensor network (EHWSN). In EHWSNs, besides the traditional issues, such as energy consumption, energy equilibrium, transmission efficiency, etc., there are still new challenges, such as how to utilize harvested energy efficiently and how to make more sensor nodes so as to achieve unlimited lifetime under actual situation. In this paper, inspired by slime mold Physarum polycephalum, a novel bionic routing protocol, abbreviated as EHPRP, is proposed for EHWSNs to address above problems without predicting harvestable energy value. Three distributed routing algorithms with low algorithm complexity are proposed which would prominently reduce the processing delay and conserve energy. Furthermore, the mathematic theoretical analysis is made to prove the stability of EHPRP routing strategy. Finally, simulation results present that, compared with other typical algorithms, EHPRP consumes less energy, always making the whole network obtain an unlimited lifetime, and displaying more uniform network energy distribution under different workload conditions.

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References

  1. Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems, 6(32), 32–70.

    Article  Google Scholar 

  2. Tan, Y. K., & Panda, S. K. (2011). Optimized wind energy harvesting system using resistance emulator and active rectifier for wireless sensor nodes. IEEE Transactions on Power Electronics, 26, 38–50.

    Article  Google Scholar 

  3. Ali, G., Wagner, J., Moline, D., & Schweisinger, T. (2015). Energy harvesting from atmospheric variations—Theory and test. Renewable Energy, 74, 528–535.

    Article  Google Scholar 

  4. Garry, S. M., & Knight, C. (2012). Development and successful application of a tree movement energy harvesting device, to power a wireless sensor node. Sensors, 12(9), 12110–12125.

    Google Scholar 

  5. Basagni, S., Naderi, M., Petrioli, C., & Spenza, D. (2013). Wireless sensor networks with energy harvesting. In Mobile Ad hoc networking: The cutting edge directions, John Wiley-IEEE Press. doi:10.1002/9781118511305.

  6. Tan, Q., An, W., Han, Y., Luo, H., Liu, Y., Ci, S., et al. (2016). Achieving energyneutral data transmission by adjusting transmission power for energyharvesting wireless sensor networks. Wireless Communications & Mobile Computing, 16(14), 2083–2097.

    Article  Google Scholar 

  7. Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-efficient control with harvesting predictions for solar-powered wireless sensor networks. Sensors. doi:10.3390/s16010053.

  8. Jakobsen, M. K., Madsen, J., & Hansen, M. R. (2010). DEHAR: A distributed energy harvesting aware routing algorithm for ad-hoc multi-hop wireless sensor networks. In IEEE international symposium on a world of wireless, mobile and multimedia networks, (pp. 1–9). IEEE Computer Society.

  9. Liu, Z., Yang, X., Zhao, P., & Wei, Y. (2016). On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks. International Journal of Distributed Sensor Networks. doi:10.1177/1550147716661941.

  10. Jiang, D., Xu, Z., Li, W., & Chen, Z. (2015). Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks. Journal of Systems & Software, 104(C), 152–165.

  11. Jiang, D., Xu, Z., Liu, J., & Zhao, W. (2016). An optimization-based robust routing algorithm to energy-efficient networks for cloud computing. Telecommunication Systems, 63(1), 89–98.

    Article  Google Scholar 

  12. Karp, B., & Kung, H. T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on mobile computing and networking (MOBICOM ’00), (pp. 243–254), New York: NY, USA, August.

  13. Yu, Y., Govindan, R., & Estrin, D. (2001). Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. Marine Pollution Bulletin, 20(1), 48.

    Google Scholar 

  14. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Hawaii international conference on system sciences (Vol. 18, pp. 8020). IEEE Computer Society.

  15. Tero, A., Takagi, S., Saigusa, T., Ito, K., Dan, P. B., Flicker, M. D., et al. (2010). Rules for biologically inspired adaptive network design. Science, 327(5964), 439–442.

    Article  Google Scholar 

  16. Tero, A., Kobayashi, R., & Nakagaki, T. (2005). A coupled-oscillator model with a conservation law for the rhythmic amoeboid movements of plasmodial slime molds. Physica D Nonlinear Phenomena., 205(1–4), 125–135.

    Article  Google Scholar 

  17. Tero, A., Kobayashi, R., & Nakagaki, T. (2007). A mathematical model for adaptive transport network in path finding by true slime mold. Journal of Theoretical Biology., 244(4), 553.

    Article  Google Scholar 

  18. Tero, A., Yumiki, K., Kobayashi, R., Saigusa, T., & Nakagaki, T. (2008). Flow-network adaptation in physarum amoebae. Theory in Biosciences, 127(2), 89–94.

    Article  Google Scholar 

  19. Kobayashi, R., Tero, A., & Nakagaki, T. (2006). Mathematical model for rhythmic protoplasmic movement in the true slime mold. Journal of Mathematical Biology, 53(2), 273–286.

    Article  Google Scholar 

  20. Li, K., Torres, C. E., Thomas, K., Rossi, L. F., & Shen, C. C. (2011). Slime mold inspired routing protocols for wireless sensor networks. Swarm Intelligence, 5(3), 183–223.

    Article  Google Scholar 

  21. Zhang, M., Xu, C., Guan, J., Zheng, R., Wu, Q., & Zhang, H. (2015). A novel physarum-inspired routing protocol for wireless sensor networks. International Journal of Distributed Sensor Networks, 2013(8), 761–764.

    Google Scholar 

  22. Houbraken, M., Demeyer, S., Staessens, D., Audenaert, P., Colle, D., & Pickavet, M. (2013). Fault tolerant network design inspired by physarum polycephalum. Natural Computing, 12(2), 277–289.

    Article  Google Scholar 

  23. Watanabe, S., & Takamatsu, A. (2014). Transportation network with fluctuating input/output designed by the bio-inspired physarum algorithm. Plos One, 9(2), e89231.

    Article  Google Scholar 

  24. Jiang, D., Xu, Z., Wang, W., Wang, Y., & Han Y. (2015). A collaborative multi-hop routing algorithm for maximum achievable rate. Journal of Network & Computer Applications, 57(C), 182–191.

  25. Jiang, D., Ying, X., Han, Y., & Lv, Z. (2016). Collaborative multi-hop routing in cognitive wireless networks. Wireless Personal Communications, 86(2), 901–923.

    Article  Google Scholar 

  26. Jiang, D., Xu, Z., & Lv, Z. (2016). A multicast delivery approach with minimum energy consumption for wireless multi-hop networks. Telecommunication Systems, 62(4), 771–782.

    Article  Google Scholar 

  27. Savvides, A. (2001). Dynamic fine-grained localization in Ad-Hoc networks of sensors.In International conference on mobile computing and networking (pp. 166–179). ACM.

  28. Rao, J., & Fapojuwo, A. (2012). A battery aware distributed clustering and routing protocol for wireless sensor networks. In Proceedings of the WCNC (pp. 1538–1543). China: Shanghai.

  29. Kansal, A., Hsu, J., Zahedi, S., et al. (2007). Power management in energy harvesting sensor networks. ACM Transaction on Embedded Computing Systems, 6(32), 32–70.

    Article  Google Scholar 

  30. Kansal, A., Potter, D., & Srivastava, M. B. (2004). Performance aware tasking for environmentally powered sensor networks. Acm Sigmetrics Performance Evaluation Review, 32(1), 223–234.

    Article  Google Scholar 

  31. Meyn, S. P., & Dsc, R. L. T. (1996). Markov chains and stochastic stability. Berlin: Springer.

    Google Scholar 

  32. Paulo, A. A., & Pereira, L. S. (2007). Prediction of spi drought class transitions using Markov chains. Water Resources Management, 21(10), 1813–1827.

    Article  Google Scholar 

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Acknowledgements

The paper is partially supported by Fundamental Research Funds for the Central Universities (ZYGX2014J099), the National Natural Science Foundation of China (Nos. 61571104, 61071124), the 6th Innovation and Entrepreneurship Leading Talents Project of Dongguan, the General Project of Scientific Research of the Education Department of Liaoning Province (No. L20150174), the Program for New Century Excellent Talents in University (No. NCET-11-0075), and Project of Science and Technology on Electronic Information Control Laboratory.

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Authors and Affiliations

  1. School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Wenyi Tang & Ke Zhang

  2. Science and Technology on Electronic Information Control Laboratory, Chengdu, 610000, China

    Wenyi Tang & Ke Zhang

  3. School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dingde Jiang

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  1. Wenyi Tang
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  2. Ke Zhang
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Correspondence to Ke Zhang or Dingde Jiang.

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Tang, W., Zhang, K. & Jiang, D. Physarum-inspired routing protocol for energy harvesting wireless sensor networks. Telecommun Syst 67, 745–762 (2018). https://doi.org/10.1007/s11235-017-0362-8

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  • DOI: https://doi.org/10.1007/s11235-017-0362-8

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