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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2020

15.10.2018 | Intelligent Biomedical Data Analysis and Processing

RETRACTED ARTICLE: Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer

verfasst von: Mohamed Abdel-Basset, Laila A. Shawky, Khalid Eldrandaly

Erschienen in: Neural Computing and Applications | Ausgabe 3/2020

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Abstract

Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm.
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Nächster Artikel RETRACTED ARTICLE: Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

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Literatur
1.
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRef
2.
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660CrossRef
3.
Knaian AN (2000) A wireless sensor network for smart roadbeds and intelligent transportation systems. Doctoral dissertation, Massachusetts Institute of Technology
4.
Hu F, Celentano L, Xiao Y (2008) Mobile, secure tele-cardiology based on wireless and sensor networks. In: Xiao Y, Chen H (eds) Mobile telemedicine. Auerbach Publications, Boca Raton, pp 81–102
5.
Khedo KK, Perseedoss R, Mungur A (2010) A wireless sensor network air pollution monitoring system. Int J Wirel Mob Netw (IJWMN) 2(2):31–45CrossRef
6.
Werner-Allen G, Lorincz K, Ruiz M, Marcillo O, Johnson J, Lees J, Welsh M (2006) Deploying a wireless sensor network on an active volcano. IEEE Internet Comput 10(2):18–25CrossRef
7.
Shamshiri R, Kalantari F, Ting KC, Thorp KR, Hameed IA, Weltzien C, Ahmad D, Shad ZM (2018) Advances in greenhouse automation and controlled environment agriculture: a transition to plant factories and urban agriculture. Int J Agric Biol Eng 11(1):1–22
8.
Sen S, Madhu B (2017) Smart agriculture: a bliss to farmers. Int J Eng Sci Res Technol 6(4):197–202
9.
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660CrossRef
10.
Townsend, C., & Arms, S. (2005). Wireless sensor networks. MicroStrain, Inc, 20(9), 15-21
11.
Youssef M, El-Sheimy N (2007) Wireless sensor network: research vs. reality design and deployment issues. In: Fifth annual conference on communication networks and services research, 2007, CNSR’07. IEEE, pp 8–9
12.
Butenko V, Nazarenko A, Sarian V, Sushchenko N, Lutokhin A (2014) Applications of wireless sensor networks in next generation networks. Telecommunication Standardization Sector of ITU. Technical report
13.
Yildirim KS, Kalayci TE, Ugur A (2008) Optimizing coverage in a k-covered and connected sensor network using genetic algorithms. In: Proceedings of the 9th WSEAS international conference on evolutionary computing. World Scientific and Engineering Academy and Society (WSEAS), pp 21–26
14.
Gage DW (1993) Sensor abstractions to support many-robot systems. In: Mobile robots VII. International society for optics and photonics, vol 1831, pp 235–247
15.
Cardei M, Wu J (2004) Coverage in wireless sensor networks. Handbook of Sensor Networks 21:201–202
16.
Ramsden D (2009) Optimization approaches to sensor placement problems. Doctoral dissertation, Rensselaer Polytechnic Institute
17.
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513CrossRef
18.
Wang H, Wu Z, Liu Y, Wang J, Jiang D, Chen L (2009) Space transformation search: a new evolutionary technique. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation. ACM, pp 537–544
19.
20.
21.
22.
Huang CF, Tseng YC (2005) The coverage problem in a wireless sensor network. Mob Netw Appl 10(4):519–528CrossRef
23.
Sun Z, Li C, Xing X, Wang H, Yan B, Li X (2017) k-degree coverage algorithm based on optimization nodes deployment in wireless sensor networks. Int J Distrib Sensor Netw 13(2):1–16CrossRef
24.
Thai MT, Wang F, Du DH, Jia X (2008) Coverage problems in wireless sensor networks: designs and analysis. Int J Sens Netw 3(3):191–200CrossRef
25.
Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L (2017) A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms, and open problems. IEEE Commun Surv Tutor 19(1):550–586CrossRef
26.
Konstantinidis A, Yang K, Zhang Q, Zeinalipour-Yazti D (2010) A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Comput Netw 54(6):960–976CrossRef
27.
Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549MathSciNetCrossRef
28.
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308CrossRef
29.
Sampson JR (1976) Adaptation in natural and artificial systems (John H. Holland). SIAM Rev 18(3):529–530CrossRef
30.
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1, pp 39–43
31.
32.
Lam AY, Li VO (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evolut Comput 14(3):381–399CrossRef
33.
Yang XS (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation, vol 7445. Springer, Berlin, pp 240–249CrossRef
34.
Shen X, Chen J, Sun Y (2006) Grid scan: a simple and effective approach for coverage issue in wireless sensor networks. In: IEEE international conference on communications, 2006. ICC’06, vol 8. IEEE, pp 3480–3484
35.
So AMC, Ye Y (2005) On solving coverage problems in a wireless sensor network using Voronoi diagrams. In: International workshop on internet and network economics, vol 3828. Springer, Berlin, pp 584–593
36.
Zou Y, Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces. In: INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications, vol 2. IEEE Societies, IEEE, pp 1293–1303
37.
Ismail WW, Manaf SA (2010) Study on coverage in wireless sensor network using grid based strategy and particle swarm optimization. In: 2010 IEEE Asia Pacific conference on circuits and systems (APCCAS). IEEE, pp 1175–1178
38.
Ab Aziz NAB, Mohemmed AW, Alias MY (2009) A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In: International conference on networking, sensing and control, 2009. ICNSC’09. IEEE, pp 602–607
39.
Aziz NAA, Mohemmed AW, Zhang M (2010) Particle swarm optimization for coverage maximization and energy conservation in wireless sensor networks. In: European conference on the applications of evolutionary computation, vol 6025. Springer, Berlin, pp 51–60
40.
Aziz NAA, Mohemmed AW, Alias MY, Aziz KA, Syahali S (2011) Coverage maximization and energy conservation for mobile wireless sensor networks: a two phase particle swarm optimization algorithm. In: 2011 sixth international conference on bio-inspired computing: theories and applications (BIC-TA). IEEE, pp 64–69
41.
Xia J (2016) Coverage optimization strategy of wireless sensor network based on swarm intelligence algorithm. In: International conference on smart city and systems engineering (ICSCSE). IEEE, pp 179–182
42.
Li XL (2003) A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China
43.
Sun H, Zhao J (2011) Application of particle sharing based particle swarm frog leaping hybrid optimization algorithm in wireless sensor network coverage optimization. J Inf Comput Sci 8(14):3181–3188
44.
Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225CrossRef
45.
Li W (2011) PSO based wireless sensor networks coverage optimization on DEMs. In: International conference on intelligent computing, vol 6839. Springer, Berlin, pp 371–378
46.
Hutchinson M, Gallant J (2000) Digital elevation models. Terrain analysis: principles and applications. Wiley, USA, pp 29–50
47.
Wang X, Wang S, Bi D (2007) Virtual force-directed particle swarm optimization for dynamic deployment in wireless sensor networks. In International conference on intelligent computing, Springer, Berlin, Heidelberg, vol 4681, pp 292–303
48.
Yildirim KS, Kalayci TE, Ugur A (2008) Optimizing coverage in a k-covered and connected sensor network using genetic algorithms. In: Proceedings of the 9th WSEAS international conference on evolutionary computing. World Scientific and Engineering Academy and Society (WSEAS), pp 21–26
49.
50.
Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy
51.
Hajjej F, Ejbali R, Zaied M (2016) An efficient deployment approach for improved coverage in wireless sensor networks based on flower pollination algorithm. In: NETCOM, NCS, WiMoNe, GRAPH-HOC, SPM, CSEIT, pp 117–129
52.
Hajjej F, Ejbali R, Zaied M (2017) Multi objective nodes placement approach in WSN based on nature inspired optimisation algorithms. In: IARIA: the second international conference on advances in sensors, actuators, metering and sensing, Nice, France, 19–23 March, pp 30–35
53.
Andaliby Joghataie A (2018) Dynamic sensor deployment in mobile wireless sensor networks using multi-agent krill herd algorithm. Doctoral dissertation
54.
Barrow JD, Davies PC, Harper CL Jr (eds) (2004) Science and ultimate reality: quantum theory, cosmology, and complexity. Cambridge University Press, Cambridge
55.
Jannes G, Piquet R, Maïssa P, Mathis C, Rousseaux G (2011) Experimental demonstration of the supersonic-subsonic bifurcation in the circular jump: a hydrodynamic white hole. Phys Rev E 83(5):056312CrossRef
56.
Wang B, Lim HB, Ma D (2009) A survey of movement strategies for improving network coverage in wireless sensor networks. Comput Commun 32(13–14):1427–1436CrossRef
57.
Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371CrossRef
58.
Cuevas A, Febrero M, Fraiman R (2004) An anova test for functional data. Comput Stat Data Anal 47(1):111–122MathSciNetCrossRef
59.
Achthoven TV, Merabet Z, Shalaby K, Van Steenbergen F (2004) Balancing productivity and environmental pressure in Egypt. Agriculture and rural development working paper. 2004(13)
60.
El Nahry AH, Elewa HH, Qaddah AA, Gedida N (2010) Soil and groundwater capability of East Oweinat area, Western Desert, Egypt using GIS spatial modeling techniques. Nat Sci 8(8):1–17
61.
62.
Gondchawar N, Kawitkar RS (2016) IoT based smart agriculture. Int J Adv Res Comput Commun Eng (IJARCCE) 5(6):177–181
63.
da Cruz MA, Rodrigues JJP, Al-Muhtadi J, Korotaev VV, de Albuquerque VHC (2018) A reference model for internet of things middleware. IEEE Internet Things J 5(2):871–883CrossRef
64.
65.
Elias ED (2013) Wireless Farming: a mobile and Wireless Sensor Network based application to create farm field monitoring and plant protection for sustainable crop production and poverty reduction. Project 30p. Malmo Hogskola. Unpublished master thesis
66.
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetCrossRef
67.
Zimmerman DW, Zumbo BD (1993) Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. J Exp Educ 62(1):75–86CrossRef
68.
Metadaten
Titel
RETRACTED ARTICLE: Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer
verfasst von
Mohamed Abdel-Basset
Laila A. Shawky
Khalid Eldrandaly
Publikationsdatum
15.10.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3807-4

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