Skip to main content
SpringerLink
Log in

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

  • Intelligent Biomedical Data Analysis and Processing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(online source: [19,20,21])

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

(online sources: [61])

Fig. 13
Fig. 14

(online sources: [68])

Fig. 15

Similar content being viewed by others

References

  1. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  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):1645–1660

    Article  Google Scholar 

  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

    Google Scholar 

  5. Khedo KK, Perseedoss R, Mungur A (2010) A wireless sensor network air pollution monitoring system. Int J Wirel Mob Netw (IJWMN) 2(2):31–45

    Article  Google Scholar 

  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):18–25

    Article  Google Scholar 

  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

    Article  Google Scholar 

  8. Sen S, Madhu B (2017) Smart agriculture: a bliss to farmers. Int J Eng Sci Res Technol 6(4):197–202

    Google Scholar 

  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):1645–1660

    Article  Google Scholar 

  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

    Google Scholar 

  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):495–513

    Article  Google Scholar 

  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. Precision Agriculture and the Internet of Things. https://censis.org.uk/2017/07/20/precision-agriculture-and-the-internet-of-things/. Accessed 30 June 2018

  20. Pycno. https://www.pycno.com. Accessed 30 June 2018

  21. WATERSENSE Project Consortium. http://www.projectwatersense.nl/. Accessed 30 June 2018

  22. Huang CF, Tseng YC (2005) The coverage problem in a wireless sensor network. Mob Netw Appl 10(4):519–528

    Article  Google Scholar 

  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):1–16

    Article  Google Scholar 

  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):191–200

    Article  Google Scholar 

  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):550–586

    Article  Google Scholar 

  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):960–976

    Article  Google Scholar 

  27. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549

    Article  MathSciNet  Google Scholar 

  28. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308

    Article  Google Scholar 

  29. Sampson JR (1976) Adaptation in natural and artificial systems (John H. Holland). SIAM Rev 18(3):529–530

    Article  MathSciNet  Google Scholar 

  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. Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11

    Article  MathSciNet  Google Scholar 

  32. Lam AY, Li VO (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evolut Comput 14(3):381–399

    Article  Google Scholar 

  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 240–249

    Chapter  Google Scholar 

  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

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Google Scholar 

  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):210–225

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  46. Hutchinson M, Gallant J (2000) Digital elevation models. Terrain analysis: principles and applications. Wiley, USA, pp 29–50

    Google Scholar 

  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. Huang P, Lin F, Liu C, Gao J, Zhou JL (2015) ACO-based sweep coverage scheme in wireless sensor networks. J Sensors. https://doi.org/10.1155/2015/484902

    Article  Google Scholar 

  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

    MATH  Google Scholar 

  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):056312

    Article  Google Scholar 

  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):1427–1436

    Article  Google Scholar 

  57. Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Article  Google Scholar 

  58. Cuevas A, Febrero M, Fraiman R (2004) An anova test for functional data. Comput Stat Data Anal 47(1):111–122

    Article  MathSciNet  Google Scholar 

  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

    Google Scholar 

  61. Cultivating Egypt’s Desert. https://earthobservatory.nasa.gov/IOTD/view.php?id=89820&eocn=image&eoci=related_image. Accessed 3 July 2018

  62. Gondchawar N, Kawitkar RS (2016) IoT based smart agriculture. Int J Adv Res Comput Commun Eng (IJARCCE) 5(6):177–181

    Google Scholar 

  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):871–883

    Article  Google Scholar 

  64. Keswani B, Mohapatra AG, Mohanty A, Khanna A, Rodrigues JJPC, Gupta D, de Albuquerque VHC (2018) Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3737-1

    Article  Google Scholar 

  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):4831–4845

    Article  MathSciNet  Google Scholar 

  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):75–86

    Article  Google Scholar 

  68. Map of East Oweinat-2012. http://digitalmapofegypt.blogspot.com/2012/. Accessed 3 July 2018

Download references

Author information

Authors and Affiliations

  1. Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, El-Zera Square, Zagazig, Sharqiyah, Egypt

    Mohamed Abdel-Basset & Laila A. Shawky

  2. Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, El-Zera Square, Zagazig, Sharqiyah, Egypt

    Khalid Eldrandaly

Authors
  1. Mohamed Abdel-Basset
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laila A. Shawky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khalid Eldrandaly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Abdel-Basset.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdel-Basset, M., Shawky, L.A. & Eldrandaly, K. Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer. Neural Comput & Applic 32, 607–624 (2020). https://doi.org/10.1007/s00521-018-3807-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3807-4

Keywords

Search

Navigation