Abstract
Accurate link quality estimation is a prerequisite for efficient routing in wireless sensor networks. Good link quality estimators should provide agility and stability simultaneously, which not only filter out transient link quality fluctuations but also respond quickly when sudden changes arise. However, only stability or agility is considered as optimization goal in existing estimators, so their performance is always below expectations. In this paper, a fluctuation adaptive link quality estimator is proposed, which adjusts smoothing factor of the estimation dynamically according to the degree of link quality fluctuations and achieves equilibrium of stability and agility. Experimental results show that stability of the proposed estimator is same as that of existing stable estimators when there are transient fluctuations, and agility of the proposed estimator is same as that of existing agile estimators when sudden changes arise. More importantly, compared with existing estimators, the estimate error of the proposed one is reduced by 22.5%–31.8% for different link characteristics.
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References
Baccour, N., et al.: Radio link quality estimation in wireless sensor networks: a survey. ACM Trans. Sens. Netw. 8(4), 1–33 (2012)
Woo A, and Culler D.: Evaluation of efficient link reliability estimators for low-power wireless networks. Technical report, UCB/CSD-03-1270, EECS Department, University of California, Berkeley (2003). https://www2.eecs.berkeley.edu/Pubs/TechRpts/2003/CSD-03-1270.pdf. Accessed 1June 2019
Huang, T., Li, D., Zhang, Z., Cui, L.: Bursty-link-aware adaptive link quality estimation method. J. Commun. 33(6), 30–39 (2012). (in Chinese)
Jayasri, T., Hemalatha, M.: Link quality estimation for adaptive data streaming in WSN. Wirel. Pers. Commun. 94, 1543–1562 (2017)
Senel, M., Chintalapudi, K., Lal, D., Keshavarzian, A., Coyle, E.J.: A Kalman filter based link quality estimation scheme for wireless sensor networks. In: IEEE Global Telecommunications Conference (GLOBECOM), pp. 875–880. IEEE, Washington D.C. (2007)
Shu, J., Tao, J., Liu, L., Chen, Y., Zang, C.: CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss. J. China Univ. Posts Telecommun. 20(1), 1–10 (2013)
Boano, C.A., ZúÑiga, M.A., Voigt, T., Willig, A., Romer, K.: The triangle metric: fast link quality estimation for mobile wireless sensor networks. In: International Conference on Computer Communications and Networks (ICCCN), pp. 1–7. IEEE, Zurich (2010)
Gomes, R.D., Queiroz, D.V., Filho, A.C.L., Fonseca, I.E., Alencar, M.S.: Real-time link quality estimation for industrial wireless sensor networks using dedicated nodes. Ad Hoc Netw. 59, 116–133 (2017)
Couto, D.S.J.D., Aguayo, D., Bicket, J., Morris, R.: A high-throughput path metric for multi-hop wireless routing. In: Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 134–146. ACM, San Diego (2003)
Fonseca, R., Gnawali, O., Jamieson, K., Levis, P.: Four bit wireless link estimation. In: Proceedings of the International Workshop on Hot Topics in Networks (HotNets VI), pp. 1–6. ACM, Atlanta (2007)
Baccour, N., Koubâa, A., Youssef, H., Ben Jamâa, M., do Rosário, D., Alves, M., Becker, L.B.: F-LQE: a fuzzy link quality estimator for wireless sensor networks. In: Silva, J.S., Krishnamachari, B., Boavida, F. (eds.) EWSN 2010. LNCS, vol. 5970, pp. 240–255. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11917-0_16
Rekik, S., Baccour, N., Jmaiel, M., Drira, K.: Low-power link quality estimation in smart grid environments. In: Proceedings of International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1211–1216. IEEE, Dubrovnik (2015)
Liu, T., Cerpa, A.E.: Foresee (4C): wireless link prediction using link features. Int. Conf. on Information Processing in Sensor Networks (IPSN), pp. 294–305. IEEE, Chicago (2011)
Liu, T., Cerpa, A.E.: TALENT: temporal adaptive link estimator with no training. In: ACM Conference on Embedded Network Sensor Systems (SenSys), pp. 253–266. ACM, Toronto (2012)
Marinca, D., Minet, P.: On-line learning and prediction of link quality in wireless sensor networks. In: IEEE Global Communications Conference (GLOBECOM), pp. 1245–1251. IEEE, Austin (2014)
MEMSIC Inc.: TelosB datasheet. http://www.memsic.cn/userfiles/files/6020-0094-02_B_TELOSB.pdf. Accessed 1 June 2019
Srinivasan, K., Dutta, P., Tavakoli, A., Levis, P.: An empirical study of low-power wireless. ACM Trans. Sens. Netw. 6(2), 1–49 (2010)
Acknowledgments
This work is supported in part by National Natural Science Foundation of China (Grant No. 61601069), Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2017jcyjAX0254), and Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJ1600935).
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Liu, W., Xia, Y., Luo, R. (2019). FaLQE: Fluctuation Adaptive Link Quality Estimator for Wireless Sensor Networks. In: Guo, S., Liu, K., Chen, C., Huang, H. (eds) Wireless Sensor Networks. CWSN 2019. Communications in Computer and Information Science, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1785-3_4
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DOI: https://doi.org/10.1007/978-981-15-1785-3_4
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