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Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 2/2022

03.08.2021 | Research Article--Computer Engineering and Computer Science

Adaptive Cuckoo Optimized WSNs Routing Algorithm Based on Forward Transmission Dynamic Programming

verfasst von: Xiuwu Yu, Ying Li, Yong Liu, Hao Yu

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 2/2022

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Abstract

In order to effectively balance the energy consumption of wireless sensor networks and ensure the real-time performance of data transmission, an adaptive cuckoo optimized WSNs routing algorithm (ACRFD) based on forward transmission dynamic programming is proposed. Firstly, the forward transmission area of the node is defined to form an energy-saving routing toward the Sink node. Secondly, according to the multi-hop characteristics of data transmission in wireless sensor networks, the energy consumption balanced WSNs dynamic programming model with minimum delay is established. Finally, the cuckoo search algorithm with adaptive search step is introduced to obtain the optimal solution of the model. The simulation results show that, compared with the other three routing algorithms, the ACRFD algorithm effectively prolongs the network lifetime, and the energy consumption is more balanced, and the real-time performance of data transmission is significantly improved.
Vorheriger Artikel UAV Communications with Machine Learning: Challenges, Applications and Open Issues
Nächster Artikel An Improved Approach for Generation of a Basic Probability Assignment in the Evidence Theory Based on Gaussian Distribution

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Literatur
1.
Xiu-wu, Y.; Hao, Y.; Yong, L.; Ren-rong, X.: A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks. Comput. Netw. 167, 106994 (2020)CrossRef
2.
Aziz, M.; Tayarani-N, M.H.; Meybodi, M.R.: A two-objective memetic approach for the node localization problem in wireless sensor networks. Genet. Program Evol. Mach. 17(4), 321–358 (2016)CrossRef
3.
El Ghazi, A.; Ahiod, B.: Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks. Appl. Intell. 48(9), 2755–2769 (2017)CrossRef
4.
Zhang, D.; Quan, L.; Lin, C., et al.: Multi-layer based multi-path routing algorithm for maximizing spectrum availability. Wirel. Netw. 24, 897–909 (2018)CrossRef
5.
Al-Ariki, H.D.; Swamy, M.N.: A survey and analysis of multipath routing protocols in wireless multimedia sensor networks. Wirel. Netw. 23(6), 1823–1835 (2017)CrossRef
6.
Selvi, M.; Velvizhy, P.; Ganapathy, S., et al.: A rule based delay constrained energy efficient routing technique for wireless sensor networks. Clust. Comput. 22, 10839–10848 (2017)CrossRef
7.
Selvi, M.; Thangaramya, K.; Ganapathy, S., et al.: An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wirel. Pers. Commun. 105(4), 1475–1490 (2019)CrossRef
8.
Sridhar, M.; Pankajavalli, P.B.: An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks. Clust. Comput. 23, 1741–1754 (2020)CrossRef
9.
Seyfollahi, A.; Ghaffari, A.: A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks. Comput. Netw. 179, 107368 (2020)CrossRef
10.
Bahuguna, Y.; Punetha, D.; Verma, P.: An analytic study of the key factors influencing the design and routing techniques of a wireless sensor network. Int. J. Interact. Multimed. Artif. Intell 4, 11–15 (2017)
11.
Fanian, F.; Rafsanjani, M.K.: A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks. Appl. Soft Comput. 89, 106115 (2020)CrossRef
12.
Radhika, M.; Sivakumar, P.: Energy optimized micro genetic algorithm based LEACH protocol for WSN. Wirel. Netw. 27(1), 27–40 (2020)CrossRef
13.
Preethiya, T.; Muthukumar, A.; Durairaj, S.: Double cluster head heterogeneous clustering for optimization in hybrid wireless sensor network. Wirel. Pers. Commun. 110(4), 1751–1768 (2020)CrossRef
14.
Sasirekha, S.; Swamynathan, S.: Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. J. Commun. Netw. 19(4), 392–401 (2017)CrossRef
15.
Lee, S.; Noh, Y.; Kim, K.: Key schemes for security enhanced teen routing protocol in wireless sensor networks. Int. J. Distrib. Sens. Netw. 9(6), 391986 (2013)CrossRef
16.
Seyfollahi, A.; Ghaffari, A.: Reliable data dissemination for the Internet of Things using Harris hawks optimization. Peer-to-Peer Netw. Appl. 13(6), 1886–1902 (2020)CrossRef
17.
Pandey, S.; Pal, P.: Spin-mi: energy saving routing algorithm based on spin protocol in WSN. Natil. Acad. Sci. Lett. 37(4), 335–339 (2014)CrossRef
18.
Yu, X.; Li, F.; Li, T.; Wu, N.; Zhou, H.: Trust-based secure directed diffusion routing protocol in wsn. J. Ambient Intell. Hum. Comput. 5, 1–13 (2020)
19.
Zhuang, L.; Xin, F.; Jingjing, Z.; Teng, L.; Yanlong, W.: An improved GRSR algorithm based on energy gradient and apit grid. J. Sens. 2016, 4027–4032 (2016)
20.
Kheroua, L.; Moussaoui, S.; Guerroumi, M.; Pathan, A.S.K.: Two energy and time-efficient data dissemination protocols for large-scale wireless sensor networks. Telecommun. Syst. 70, 81–96 (2018)CrossRef
21.
Wang, J.; Gao, Y.; Wang, K.; Sangaiah, A.K.; Li, S.-J.: An affinity propagation-based self-adaptive clustering method for wireless sensor networks. Sensors 19(11), 2579 (2019)CrossRef
22.
Hu, Y.; Niu, Y.: An energy-efficient overlapping clustering protocol in WSNs. Wirel. Netw. 24(5), 1775–1791 (2016)CrossRef
23.
Yang, X.S.; Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Modell. Num. Opt. 1(4), 330–343 (2010)MATH
24.
Munuswamy, S., et al.: Virtual force-based intelligent clustering for energy-efficient routing in mobile wireless sensor networks. Turk. J. Electr. Eng. Comput. Sci. 26(3), 1444–1452 (2018)
25.
Shyjith, M.B.; Maheswaran, C.P.; Reshma, V.K.: Optimized and dynamic selection of cluster head using energy efficient routing protocol in WSN. Wirel. Pers. Commun. 116, 577–599 (2020)CrossRef
26.
Ilyas, M.U.; Radha, H.: Increasing network lifetime of an IEEE 802.15.4 wireless sensor network by energy efficient routing. In: 2006 IEEE International Conference on Communications, vol. 9, pp. 3978–3983. IEEE (2006)
27.
Kolli, S.; Zawodniok, M.: A dynamic programming approach: improving the performance of wireless networks. J. Parallel Distrib. Comput. 71(11), 1447–1459 (2011)MATHCrossRef
28.
Ilyas, M.U.; Radha, H.: A dynamic programming approach to maximizing a statistical measure of the lifetime of sensor networks. ACM Trans. Sens. Netw. 8(2), 1–21 (2012)CrossRef
29.
Wang, X.L.; Li, L.Y.: Routing algorithm based on dynamic programming in wireless sensor networks. Comput. Eng. 38(11), 114–116 (2012)
30.
Du, R.; Gkatzikis, L.; Fischione, C.; Xia, M.: Energy efficient sensor activation for water distribution networks based on compressive sensing. Energy Effic. Sens. Activ. Water Distrib. Netw. Based Compress. Sens. 33(12), 2997–3010 (2015)
31.
Gogu, A.; Nace, D.; Natalizio, E.; Challal, Y.: Using dynamic programming to solve the wireless sensor network configuration problem. J. Netw. Comput. Appl. 83, 140–154 (2017)CrossRef
32.
Dehwah, A.H.; Shamma, J.S.; Claudel, C.G.: A distributed routing scheme for energy management in solar powered sensor networks. Ad Hoc Netw. 67, 11–23 (2017)CrossRef
33.
Al-Karaki, J.N.; Gawanmeh, A.: The optimal deployment, coverage, and connectivity problems in wireless sensor networks: revisited. IEEE Access 5, 18051–18065 (2017)CrossRef
34.
Jayanthi, N.; Valluvan, K.R.: Hybrid routing algorithm for improving path selection in sustainable network. Clust. Comput. 101, 2365–2381 (2018)
35.
Walton, S.; Hassan, O.; Morgan, K.; Brown, M.R.: Modified cuckoo search: A new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9), 710–718 (2011)CrossRef
36.
Sheriba, S.T.; Rajesh, D.H.: Improved hybrid cuckoo black widow optimization with interval type 2 fuzzy logic system for energy-efficient clustering protocol. Int. J. Commun. Syst. 34, e4730 (2021)CrossRef
37.
Mittal, N., et al.: Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks. Wirel. Netw. 27(1), 151–174 (2021)CrossRef
38.
Yu, X.; Hu, M.: Hop-count quantization ranging and hybrid cuckoo search optimized for DV-HOP in WSNs. Wirel. Pers. Commun. 108(4), 2031–2046 (2019)CrossRef
39.
Narawade, V.; Kolekar, U.D.: ACSRO: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alex. Eng. J. 57(1), 131–145 (2016)CrossRef
40.
Mehetre, D.C.; Roslin, S.E.; Wagh, S.J.: Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust. Clust Comput 22, 1313–1328 (2018)CrossRef
41.
Ghosh, A.; Chakraborty, N.: A novel residual energy-based distributed clustering and routing approach for performance study of wireless sensor network. Int J Commun Syst 32(12), e3921 (2019)CrossRef
42.
Khabiri, M.; Ghaffari, A.: Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Pers. Commun. 98(3), 2473–2495 (2018)CrossRef
43.
Vinodhini, R.; Gomathy, C.: MOMHR: a dynamic multi-hop routing protocol for WSN using heuristic based multi-objective function. Wireless Pers. Commun. 111(2), 883–907 (2019)CrossRef
Metadaten
Titel
Adaptive Cuckoo Optimized WSNs Routing Algorithm Based on Forward Transmission Dynamic Programming
verfasst von
Xiuwu Yu
Ying Li
Yong Liu
Hao Yu
Publikationsdatum
03.08.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 2/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-05988-8

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