Abstract
The wide application of wireless sensor network facilitates a great variety of utilizations including remote monitoring, air condition evaluating, tracking and targeting, and so on. However, the performance of a wireless network is constraint by low-power and low-capacity. Hence, long-distance communication is not available for a homogeneous network, which hinders the growth of wireless network. This work proposed a strategy for deploying Relay nodes into the network based on directional shuffle frog leaping algorithm. The Relay node is an advanced node which is more powerful than common node and it can decrease the workload of inner nodes and improve the transmission situation of outer nodes. The experiment simulates other two algorithms as the comparison tests. The performance is good as evidenced by the experiment results of common nodes coverage, connectivity of Relay nodes and the fitness value.
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Dapeng, W., Jing, H., Honggang, W., Chonggang, W., Ruyan, W.: A hierarchical packet forwarding mechanism for energy harvesting wireless sensor networks. IEEE Commun. Mag. 53(8), 92–98 (2015)
Lloyd, E.L., Guoliang, X.: Relay node placement in wireless sensor networks. IEEE Trans. Comput. 56(1), 134–138 (2007)
Yung, F.H., Chung-Hsin, H.: Energy efficiency of dynamically distributed clustering routing for naturally scattering wireless sensor networks. J. Netw. Intell. 3(1), 50–57 (2018)
Younis, M., Kemal, A.: Strategies and techniques for node placement in wireless sensor networks: a survey. Ad Hoc Netw. 6(4), 621–655 (2008)
Liquan, Z., Nan, C.: An effective clustering routing protocol for heterogeneous wireless sensor networks. J. Inf. Hiding Multimed. Signal Process. 8(3), 723–733 (2017)
Senel, F., Mohamed, F.Y., Kemal, A.: Bio-inspired relay node placement heuristics for repairing damaged wireless sensor networks. IEEE Trans. Veh. Technol. 60(4), 1835–1848 (2011)
Gwo-Jiun, H., Tun-Yu, C., Hsin-Te, W.: The adaptive node-selection mechanism scheme in solar-powered wireless sensor networks. J. Netw. Intell. 3(1), 58–73 (2018)
Dejun, Y., Satyajayant, M., Xi, F., Guoliang, X., Junshan, Z.: Two-tiered constrained relay node placement in wireless sensor networks: computational complexity and efficient approximations. IEEE Trans. Mob. Comput. 11(8), 1399–1411 (2012)
Chin-Shiuh, S., Van-Oanh, S., Tsair-Fwu, L., Quang-Duy, L., Yuh-Chung, L., Trong-The, N.: Node localization in WSN using heuristic optimization approaches. J. Netw. Intell. 2(3), 275–286 (2017)
Hashim, A., Babajide, O.A., Mohamed, A.A.: Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. J. Netw. Comput. Appl. 64, 239–248 (2016)
Eusuff, M., Kevin, L., Fayzul, P.: Shuffled frog-leaping algorithm: a memetic metaheuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)
Rahimi-Vahed, A., Ali, H.M.: Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. Soft Comput. 12(5), 435–452 (2008)
Fang, C., Ling, W.: An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Comput. Oper. Res. 39(5), 890–901 (2012)
Elbeltagi, E., Tarek, H., Donald, G.: A modified shuffled frog-leaping optimization algorithm: applications to project management. Struct. Infrastruct. Eng. 3(1), 53–60 (2007)
Kaur, P., Shikha, M.: Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. J. Parallel Distrib. Comput. 101, 41–50 (2017)
Anandamurugan, S., Abirami, T.: Antipredator adaptation shuffled frog leap algorithm to improve network life time in wireless sensor network. Wirel. Pers. Commun. 94, 1–12 (2017)
Lingping, K., Jeng-Shyang, P., Shu-Chuan, C., John, F.R.: Directional shuffled frog leaping algorithm. In: International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, pp. 257–264. Springer (2017)
Xuncai, Z., Xuemei, H., Guangzhao, C., Yanfeng, W., Ying, N.: An improved shuffled frog leaping algorithm with cognitive behavior. In: Intelligent Control and Automation, pp. 6197–6202. IEEE (2008)
Catania, A. C.: Thorndike’s legacy: learning, selection, and the law of effect. J. Exp. Anal. Behav. 72(3), 425–428 (1999)
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Kong, L., Snášel, V. (2019). A Strategy of Deploying Constant Number Relay Node for Wireless Sensor Network. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_17
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DOI: https://doi.org/10.1007/978-3-030-03766-6_17
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