# A Learning Automata Based Stable and Energy-Efficient Routing Algorithm for Discrete Energy Harvesting Mobile Wireless Sensor Network

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## Abstract

Wireless sensor networks (WSN) have been widely used in urban network system and networked monitoring system, which provide easy connectivity and high physical data rate. Considering the battery-limited property of sensor nodes, recently, energy harvesting (EH) technology is introduced into WSN, which can alleviate traditional WSN problems (energy consumption, energy equilibrium, transmission efficiency, etc.). Current EH-WSN routing algorithms generally use the continuous energy harvesting mode, therefore, how to design an efficient routing algorithm for discrete energy harvesting mode and ensure the overall energy balance and conservation is still a great challenge and needs to be solved. Especially, under the mobile environment, the impact of route stability needs to be considered, which makes the design more complicated. To address the above problems, we propose a learning automata (LA) theory based stable and energy-efficient routing algorithm for discrete EH-mobile WSN (DEH-LA-SERA, for short). Firstly, we construct a multi-factors measurement model for sensor nodes, which contains node stability model, energy ratio function, expected harvesting energy model (using Markov decision process method) and direction judgement model. On this basis, we derive the node weighted value, i.e., selecting probability, which can be used to determine whether a node can be chosen as relay node. Secondly, with the help of LA theory, we construct a feedback mechanism to adjust the optimal path. With this solution, we can ensure the overall energy balance and conservation while holding the stability of selected path. As demonstrated in simulation experiments, our algorithm, DEH-LA-SERA, achieved the best performance in route survival time, energy consumption, energy balance and acceptable performance in end-to-end delay and packets delivery ratio.

## Keywords

Energy harvesting wireless sensor network Mobile environment Routing algorithm Multi-factors measurement model Markov decision process Learning automata theory Stability and energy optimization## Notes

### Acknowledgements

The work is supported by the National Natural Science Foundation of China (No.61772386).

## References

- 1.Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey.
*Computer Networks*,*38*(4), 393–422.Google Scholar - 2.Al-Karaki, J. N., & Kamal, A. E. (2004).
*Routing techniques in wireless sensor networks: A survey*(pp. 6–28). Piscataway: IEEE Press.Google Scholar - 3.Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review.
*Renewable and Sustainable Energy Reviews*,*55*, 1041–1054.Google Scholar - 4.Sarma, H. K. D., Mall, R., & Kar, A. (2016). \(\text{ E }^2 \text{ R }^2\): Energy-efficient and reliable routing for mobile wireless sensor networks.
*IEEE Systems Journal*,*10*(2), 604–616.Google Scholar - 5.Sarma, H. K. D., Kar, A., & Mall, R. (2016). A hierarchical and role based secure routing protocol for mobile wireless sensor networks.
*Wireless Personal Communications*,*90*(3), 1067–1103.Google Scholar - 6.Tamandani, Y. K., & Bokhari, M. U. (2015). SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network.
*Wireless Networks*,*22*(2), 1–7.Google Scholar - 7.Ye, R., Boukerche, A., Wang, H., Zhou, X., & Yan, B. (2017). \(\text{ E }^3\)TX: an energy-efficient expected transmission count routing decision strategy for wireless sensor networks.
*Wireless Networks*,*3*, 1–14.Google Scholar - 8.Li, F., & Wang, L. (2018). Energy-aware routing algorithm for wireless sensor networks with optimal relay detecting.
*Wireless Personal Communications*,*98*(2), 1701–1717.Google Scholar - 9.Mottaghinia, Z., & Ghaffari, A. (2018). Fuzzy logic based distance and energy-aware routing protocol in delay-tolerant mobile sensor networks.
*Wireless Personal Communications*,*3*, 1–20.Google Scholar - 10.Khasawneh, A., Latiff, M. S. B. A., Kaiwartya, O., & Chizari, H. (2017). A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network.
*Wireless Networks, 24*(6), 2061–2075.Google Scholar - 11.Kansal, A., & Srivastava, M. B. (2003). An environmental energy harvesting framework for sensor networks. In
*Proceedings of international symposium on low power electronics and design*(pp. 481–486), Seoul, South Korea. IEEE.Google Scholar - 12.Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks.
*ACM Transactions on Embedded Computing Systems*,*6*(4), 1–35.Google Scholar - 13.Tan, Y. K., & Panda, S. K. (2010). Optimized wind energy harvesting system using resistance emulator and active rectifier for wireless sensor nodes.
*IEEE Transactions on Power Electronics*,*26*(1), 38–50.Google Scholar - 14.Kimball, J. W., Kuhn, B. T., & Balog, R. S. (2009). A system design approach for unattended solar energy harvesting supply.
*IEEE Transactions on Power Electronics*,*24*(4), 952–962.Google Scholar - 15.Muhammad, U. B., Ezugwu, A. E., Ofem, P. O., Rajamäki, J., & Aderemi, A. O. (2017). Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network.
*Proceedings of American Institute of Physics Conference Series*,*1836*(1), 921–960.Google Scholar - 16.Tang, W., Zhang, K., & Jiang, D. (2018). Physarum-inspired routing protocol for energy harvesting wireless sensor networks.
*Telecommunication Systems*,*32*, 1–18.Google Scholar - 17.Liu, Z., Yang, X., Zhao, P., & Yu, W. (2016). On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks.
*International Journal of Distributed Sensor Networks*,*8*(12), 1–11.Google Scholar - 18.Lu, T., Liu, G., & Chang, S. (2018). Energy-efficient data sensing and routing in unreliable energy-harvesting wireless sensor network.
*Wireless Networks*,*24*(2), 611–625.Google Scholar - 19.Hieu, T. D., Dung, l T., & Kim, B. S. (2016). Stability-aware geographic routing in energy harvesting wireless sensor networks.
*Sensors*,*16*(5), 1–15.Google Scholar - 20.Sun, G., Shang, X., & Zuo, Y. (2018). La-CTP: Loop-aware routing for energy-harvesting wireless sensor networks.
*Sensors*,*18*(2), 434–453.Google Scholar - 21.Tang, J., Liu, A., Zhang, J., et al. (2018). A trust-based secure routing scheme using the traceback approach for energy-harvesting wireless sensor networks.
*Sensors*,*18*(3), 751–793.Google Scholar - 22.Chin, K. W., Wang, L., & Soh, S. (2016). Joint routing and links scheduling in two-tier multi-hop RF-energy harvesting networks.
*IEEE Communications Letters*,*20*(9), 1864–1867.Google Scholar - 23.Ashraphijuo, M., Aggarwal, V., & Wang, X. (2015). On the capacity of energy harvesting communication link.
*IEEE Journal on Selected Areas in Communications*,*33*(12), 2671–2686.Google Scholar - 24.Trillingsgaard, K. F., & Popovski, P. (2014). Communication strategies for two models of discrete energy harvesting In
*Proceedings of IEEE international conference on communications*(pp. 2081–2086), Sydney, NSW, Australia. IEEE.Google Scholar - 25.Narendra, K. S., & Thathachar, M. A. L. (2012). Learning automata: An introduction. USA:DBLP.Google Scholar
- 26.Thathachar, M. A. L., & Sastry, P. S. (1987).
*A hierarchical system of learning automata that can learn the globally optimal path*. New York: Elsevier Science Inc.zbMATHGoogle Scholar - 27.Beigy, H., & Meybodi, M. R. (2011). Utilizing distributed learning automata to solve stochastic shortest path problems.
*International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems*,*14*(05), 591–615.MathSciNetzbMATHGoogle Scholar - 28.Kim, J., & Lee, J. W. (2017).
*Energy adaptive MAC for wireless sensor networks with RF energy transfer: Algorithm, analysis, and implementation*(pp. 1–15). Alphen aan den Rijn: Kluwer Academic Publishers.Google Scholar - 29.Guo, S., Shi, Y., Yang, Y., et al. (2017). Energy efficiency maximization in mobile wireless energy harvesting sensor networks.
*IEEE Transactions on Mobile Computing*,*PP*(99), 1–1.Google Scholar - 30.Huang, L. (2017). Optimal sleep-wake scheduling for energy harvesting smart mobile devices.
*IEEE Transactions on Mobile Computing*,*14*(2), 1394–1407.Google Scholar - 31.Zhang, H., Huang, S., Jiang, C., et al. (2017). Energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations.
*IEEE Journal on Selected Areas in Communications*,*PP*(99), 1–1.Google Scholar - 32.West, D. B. (2005).
*Introduction to graph theory*(2nd ed., p. 260). New York: McGraw-Hill Higher Education.Google Scholar - 33.Zonoozi, M. M., & Dassanayake, P. (1997). User mobility modeling and characterization of mobility patterns.
*IEEE Journal on Selected Areas in Communications*,*15*(7), 1239–1252.Google Scholar - 34.Mcdonald, A. B., & Znati, T. (1999). A path availability model for wireless ad-hoc networks. In
*Proceedings of wireless communications and networking conference*(pp. 35–40). New Orleans, LA, USA. IEEE.Google Scholar - 35.Biswas, S., & Datta, S. (2004). Reducing overhearing energy in 802.11 networks by low-power interface idling. In
*Proceedings of IEEE international conference on performance*(pp. 695-700), Phoenix, AZ, USA. IEEE.Google Scholar - 36.Le, H. C., Guyennet, H., & Felea, V. (2007). OBMAC: An overhearing based MAC protocol for wireless sensor networks. In
*Proceedings of international conference on sensor technologies and applications*(pp. 547–553), Valencia, Spain. IEEE.Google Scholar - 37.Riley, G. F., & Henderson, T. R. (2010). The ns-3 network simulator. In
*Modeling and tools for network simulation*(pp. 15–34).Google Scholar - 38.Kushner, H. J. (1984).
*Approximation and weak convergence methods for random processes, with applications to stochastic systems theory*. Cambridge: MIT Press.zbMATHGoogle Scholar