Skip to main content

Advertisement

SpringerLink
Log in

Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Recently, the wireless sensor networks (WSNs) found its extensive application in surveillance and target tracking. For these two WSN applications, connectivity and coverage play a major role most particularly for target tracking. A large number of available sensor nodes track targets, during which a massive redundant data gets generated, which may minimize the system performance. Most particularly during the sensor node failure, the major intention of coverage and connectivity optimization model is to select less number of sensor nodes with maximum direct sensor node connectivity. But existing algorithms fail to achieve minimal node selection, therefore to mitigate the barriers of the traditional coverage algorithms, this paper proposed the hybrid Gravitational Search algorithm with social ski-driver (GSA-SSD) based model. This hybrid approach in target based WSN optimizes the coverage and connectivity requirement. By adapting the dynamic behaviour of SSD algorithm, the performance of GSA gets improved. Finally, the relative performance of the proposed hybrid GSA-SSD based optimization model is validated and compared with other optimization algorithms. On the basis of uncovered area rate and a number of sensor nodes the performance is evaluated. The results are implemented in the MATLAB simulation tool. Further, the performance enhancement in terms of uncovered area rate, number of selected active sensors, energy consumption, connectivity and network lifetime is achieved with randomly deployed nodes.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Abu-Mahfouz AM (2018) Localised information fusion techniques for location discovery in wireless sensor networks. Intern J Sens Netw 26:12–25

    Article  Google Scholar 

  • Ahmad T, Haque M, Khan AM (2019) An energy-efficient cluster head selection using artificial bees colony optimization for wireless sensor networks. Advances in nature-inspired computing and applications. Springer, Cham, pp 189–203

    Chapter  Google Scholar 

  • Ammari HM, Das SK (2012) Centralized and clustered k-coverage protocols for wireless sensor networks. IEEE Trans Comput 61(1):118–133

    Article  MathSciNet  Google Scholar 

  • Aziz AN, Aziz KA, Ismail WZW (2009) Coverage strategies for wireless sensor networks. World Acad Sci Eng Technol 50:145–150

    Google Scholar 

  • Bi K, Tu K, Gu N, Dong W (2006) Topological hole detection in sensor networks with cooperative neighbors. Proc Intern Conf Syst 00(60533020):1–5

    Google Scholar 

  • Cheng C-F, Tsai K-T (2017) Encircled belt-barrier coverage in wireless visual sensor networks. Perv Mob Comput 38:233–256

    Article  Google Scholar 

  • Dahiya S, Singh PK (2019) Energy efficient SOCGO protocol for hole repair node scheduling in reliable sensor system. Wireless Personal Commun pp 1–21

  • Dash D, Gupta A, Bishnu A, Nandy SC (2014) Line coverage measures in wireless sensor networks. J Parallel Distrib Comput 74(7):2596–2614

    Article  Google Scholar 

  • Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153

    Article  Google Scholar 

  • Gorain B, Mandal PS (2017a) Solving energy issues for sweep coverage in wireless sensor networks. Discr Appl Mathematics 228:130–139

    Article  MathSciNet  Google Scholar 

  • Gorain B, Mandal PS (2017b) Solving energy issues for sweep coverage in wireless sensor networks. Discrete Applied Mathematics 228:130–139

    Article  MathSciNet  Google Scholar 

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670

    Article  Google Scholar 

  • Jehan C, Punithavathani DS (2017) Potential position node placement approach via oppositional gravitational search for fulfill coverage and connectivity in target based wireless sensor networks. Wireless Netw 23(6):1875–1888

    Article  Google Scholar 

  • Khan I, Mokhtar H and Merabti M (2008) A survey of boundary detection algorithms for sensor networks. In: Proceedings of the 9th Annual Postgraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting

  • Kim D, Kim Y, Li D, Seo J (2017) A new maximum fault-tolerance barrier-coverage problem in hybrid sensor network and its polynomial time exact algorithm. Ad Hoc Netw 63:14–19

    Article  Google Scholar 

  • Krishnan M, Rajagopal V, Rathinasamy S (2018) Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs. Wireless Netw 24(3):683–693

    Article  Google Scholar 

  • Kulkarni R, Forster A, Venayagamoorthy G (2011) Computational intelligence in wireless sensor networks: a survey. IEEE Commun Surv Tutorials 13(1):68–96

    Article  Google Scholar 

  • Lehsaini M and Benmahdi MB (2018) An improved k-means cluster-based routing scheme for wireless sensor networks. In: 2018 International Symposium on Programming and Systems (ISPS), IEEE, pp 1–6

  • Li H, Wang S, Gong M, Chen Q, Chen L (2017) IM 2 DCA: Immune mechanism based multipath decoupling connectivity algorithm with fault tolerance under coverage optimization in wireless sensor networks. Appl Soft Computing 58:540–552

    Article  Google Scholar 

  • Mansour M, Jarray F (2015) An iterative solution for the coverage and connectivity problem in wireless sensor network. Procedia Comput Sci 63:494–498

    Article  Google Scholar 

  • Mini S, Udgata S, Sabat S (2014) Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens J 14(3):636–644

    Article  Google Scholar 

  • Mohamed SM, Hamza HS, Saroit IA (2017) Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput Commun 110:133–150

    Article  Google Scholar 

  • More A, Raisinghani V (2017) A survey on energy efficient coverage protocols in wireless sensor networks. J King Saud Univ Compu Inform Sci 29(4):428–448

    Google Scholar 

  • Mostafaei H (2015) Stochastic barrier coverage in wireless sensor networks based on distributed learning automata. Comput Commun 55:51–61

    Article  Google Scholar 

  • Movassagh M, Aghdasi HS (2017) Game theory based node scheduling as a distributed solution for coverage control in wireless sensor networks. Eng Appl Artif Intell 65:137–146

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  • Rebai M, Le Berre M, Snoussi H, Hnaien F, Khoukhi L (2015) Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Comput Opera Res 59:11–21

    Article  MathSciNet  Google Scholar 

  • Sangwan A, Singh R (2014) Survey on coverage problems in wireless sensor networks. Wireless Pers Commun 80(4):1475–1500

    Article  Google Scholar 

  • Tharwat A, Gabel T (2019) Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput Appl 1–4

  • Wang W, Srinivasan V, Wang B, Chua KC (2008) Coverage for target localization in wireless sensor networks. IEEE Trans Wireless Commun 7(2):667–676

    Article  Google Scholar 

  • Wang Y, Wu S, Chen Z, Gao X, Chen G (2017) Coverage problem with uncertain properties in wireless sensor networks: a survey. Comput Netw 123:200–232

    Article  Google Scholar 

  • Wang J, Cao J, Sherratt RS, Park JH (2018a) An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. The J Supercomput 74(12):6633–6645

    Article  Google Scholar 

  • Wang J, Gao Y, Yin X, Li F, Kim HJ (2018b) An enhanced PEGASIS algorithm with mobile sink support for wireless sensor networks. Wireless Commun Mobile Comput 2018:1–9

    Google Scholar 

  • Wang J, Ju C, Kim HJ, Sherratt RS, Lee S (2019a) A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs. Cluster Comput 22(1):1787–1795

    Article  Google Scholar 

  • Wang J, Gao Y, Liu W, Wu W, Lim SJ (2019b) An asynchronous clustering and mobile data gathering schema based on timer mechanism in wireless sensor networks. Comput Mat Cont 58:711–725

    Google Scholar 

  • Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019c) An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. Int J Distrib Sens Netw 15(3):1550147719839581

    Google Scholar 

  • Wang J, Gu X, Liu W, Sangaiah AK, Kim HJ (2019d) An empower hamilton loop based data collection algorithm with mobile agent for WSNs. Hum Centric Comput Inform Sci 9(1):1–14

    Article  Google Scholar 

  • Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019e) Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors 19(7):1494

    Article  Google Scholar 

  • Wang J, Gao Y, Zhou C, Sherratt S, Wang L (2020) Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs. Comput Mat Cont 62(2):695–711

    Google Scholar 

  • Yan F, Ma W, Shen F, Xia W, Shen L (2019) Connectivity based k-coverage hole detection in wireless sensor networks. Mob Netw Appl 1–1

  • Yeasmin N (2014) K-coverage problems and solutions in wireless sensor networks: a survey. Intern J Comput Appl 100(17):1–6

    Google Scholar 

  • Yetgin H, Cheung K, El-Hajjar M, Hanzo L (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutorials 19(2):828–854

    Article  Google Scholar 

  • Yue Y, Cao L and Luo Z (2019) Hybrid artificial bee colony algorithm for improving the coverage and connectivity of wireless sensor networks. Wireless Personal Commun 1–14

  • Zhu C, Zheng C, Shu L, Han G (2012) A survey on coverage and connectivity issues in wireless sensor networks. J Netw Compu Appl 35(2):619–632

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication, AIKTC, New Panvel, Navi Mumbai, Maharashtra, India

    Chaya Shivalingegowda

  2. E&C Department, GITAM University, Visakhapatnam, Andhra Pradesh, India

    P. V. Y. Jayasree

Authors
  1. Chaya Shivalingegowda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. V. Y. Jayasree
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaya Shivalingegowda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shivalingegowda, C., Jayasree, P.V.Y. Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks. J Ambient Intell Human Comput 12, 2835–2848 (2021). https://doi.org/10.1007/s12652-020-02442-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02442-9

Keywords

Search

Navigation