Abstract
Structural health monitoring (SHM) is a kind of data-intensive applications for wireless sensors networks, which usually requires high network bandwidth. However, the bandwidth of traditional single-radio single-channel (SR-SC) WSN is quite limited. In order to meet the requirement of structural monitoring, multi-radio multi-channel (MR-MC) WSN is emerging. In this paper, we address the optimal data collection problem in MR-MC WSN by modelling it as an integer linear programming problem. Combining the advantages of the particle swarm optimization (PSO) algorithm and flower pollination optimization (FPA) algorithm, we propose a new hybrid algorithm BFPA-PSO to solve the optimization problem under the constraint of time slot and multi-power multi-radio multi-channel (MP-MR-MC). Theoretical analysis and simulation experiments are carried out and the results show that the proposed method has good performance in improving network capacity as well as reducing energy consumption.
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References
Risodkar, Y.R., Pawar, A.S.: A survey: structural health monitoring of bridge using WSN. In: International Conference on Global Trends in Signal Processing. Information Computing and Communication, Jalgaon, pp. 615–618. IEEE (2017)
Chen, Z., Casciati, F.: A low-noise, real-time, wireless data acquisition system for structural monitoring applications. Struct. Control. Health Monit. 21(7), 1118–1136 (2014)
Chen, Z.C., Casciati, S., Faravelli, L.: In-situ validation of a wireless data acquisition system by monitoring a pedestrian bridge. Adv. Struct. Eng. 18(1), 97–106 (2014)
Kim, S., Pakzad, S., Culler, D.: Health monitoring of civil infrastructures using wireless sensor networks. In: International Symposium on Information Processing in Sensor Networks, Cambridge, pp. 254–263. IEEE (2007)
Mehrabi, A., Kim, K.: General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks. IEEE Trans. Mob. Comput. 16(7), 1881–1896 (2017)
Lian, J., Naik, K., Agnew, G.B.: Data capacity improvement of wireless sensor networks using non-uniform sensor distribution. Int. J. Distrib. Sens. Netw. 2(2), 121–145 (2006)
Ji, S., Cai, Z., Li, Y., Jia, X.: Continuous data collection capacity of dual-radio multi-channel wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(10), 1844–1855 (2012)
Kodialam, M., Nandagopal, T.: Characterizing the capacity region in multi-radio multi-channel wireless mesh networks. In: International Conference on Mobile Computing and Networking, Cologne, pp. 73–87. DBLP (2005)
Fan, B., Li, J., Guo, L., Liu, X.: A multi-power multi-channel data collection scheduling algorithm in dual-radio sensor networks. Telecommun. Sci. 28(2), 36–45 (2012)
Babatunde, O., Armstrong, L., Leng, J., Diepeveen, D.: A genetic algorithm-based feature selection. Br. J. Math. Comput. Sci. 5(4), 889–905 (2014)
Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2014)
Rodrigues, D., Yang, X.-S., de Souza, A.N., Papa, J.P.: Binary flower pollination algorithm and its application to feature selection. In: Yang, X.-S. (ed.) Recent Advances in Swarm Intelligence and Evolutionary Computation. SCI, vol. 585, pp. 85–100. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13826-8_5
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Natural Comput. 9(3), 727–745 (2010)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems. Man, and Cybernetics, Orlando, pp. 4104–4108. IEEE (1997)
Chen, Z.C.: Energy efficiency strategy for a general real-time wireless sensor platform. Smart Struct. Syst. 14(4), 617–641 (2014)
Li, J., Guo, X., Guo, L.: Joint routing, scheduling and channel assignment in multi-power multi-radio wireless sensor networks. In: IEEE International Performance Computing and Communications Conference, Orlando, pp. 1–8. IEEE (2011)
Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Trans. Inf. Theory 46(2), 388–404 (2002)
Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, vol. 4, pp. 1942–1948. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-1153-7_200581
Vijayalakshmi, K., Anandan, P.: A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Comput. 6, 1–8 (2018)
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Li, Q., Chen, Z., Wu, L., Cheng, S., Lin, P. (2018). Optimal Data Collection of MP-MR-MC Wireless Sensors Network for Structural Monitoring. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_8
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