An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks


Recently, many researchers have paid attention to wireless sensor networks (WSNs) due to their ability to encourage the innovation of the IT industry. Although WSN provides dynamically scalable solutions with various smart applications, the growing need to maximize the area coverage with decreasing the percentage of deployed sensor nodes is still required. Random deployment is preferable for large areas that require a maximal number of nodes but result in coverage holes. As a result, mobile nodes are used to reduce coverage holes and maximize area coverage. The main objective of this study is to present an Improved Dynamic Deployment Technique based-on Genetic Algorithm (IDDT-GA) to maximize the area coverage with the lowest number of nodes as well as minimizing overlapping area between neighboring nodes. A two-point crossover novel is introduced to demonstrate the notation of variable-length encoding. Simulation results reveal that the superiority of the proposed IDDT-GA compared with other state-of-the-art techniques. IDDT-GA has better coverage rates with 9.69% and a minimum overlapping ratio with 35.43% compared to deployment based on Harmony Search (HS). Also, IDDT-GA has minimized the network cost by 13% and 7.44% than Immune Algorithm (IA) and Whale Optimization Algorithm (WOA) respectively. Besides, it confirms its stability with 83.04% compared to maximizing coverage with WOA.

This is a preview of subscription content, access via your institution.

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
Fig. 14


  1. Abo-Zahhad M, Ahmed SM, Sabor N, Sasaki S (2014) Coverage maximization in mobile wireless sensor networks utilizing immune node deployment algorithm. In: Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on IEEE, pp 1–6.

  2. Ali A, Ming Y, Chakraborty S, Iram S (2017) A comprehensive survey on real-time applications of wsn. Future Internet 9(4):77.

    Article  Google Scholar 

  3. Aponte-Luis J, Gómez-Galán JA, Gómez-Bravo F, Sánchez-Raya M, Alcina-Espigado J, Teixido-Rovira PM (2018) An efficient wireless sensor network for industrial monitoring and control. Sensors 18(1):182.

    Article  Google Scholar 

  4. Bala T, Bhatia V, Kumawat S, Jaglan V (2018) A survey: issues and challenges in wireless sensor network. Int J Eng Technol 7(24).

  5. Banoori F, Kashif M, Arslan M, Chakma R, Khan F, Al Mamun A (2018) Deployment techniques of nodes in wsn and survey on their performance analysis. In: 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018). Atlantis Press.

  6. Binh HTT, Hanh NT, Dey N et al (2018) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30(7):2305–2317.

    Article  Google Scholar 

  7. Boualem A, Dahmani Y, Maatoug A, De Runz C (2018) Area coverage optimization in wireless sensor network by semi-random deployment. In: SENSORNETS, pp 85–90.

  8. Chien TV, Chan HN, Huu TN (2012) A comparative study on hardware platforms for wireless sensor networks. Int J Adv Sci Eng Inf Technol 2(1):70–74.

    Article  Google Scholar 

  9. Du K-L, Swamy M (2016) Simulated annealing. In: Search and Optimization by Metaheuristics, Springer, pp 29–36.

  10. El Khamlichi Y, Tahiri A, Abtoy A, Medina-Bulo I, Palomo-Lozano F (2017) A hybrid algorithm for optimal wireless sensor network deployment with the minimum number of sensor nodes. Algorithms 10(3):80.

    MathSciNet  Article  MATH  Google Scholar 

  11. Elma KJ, Meenakshi S (2019) Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network. In: Journal of Ambient Intelligence and Humanized Computing, pp 1–12.

  12. Ezhilarasi M, Krishnaveni V (2018) A survey on wireless sensor network: energy and lifetime perspective. In: Taga Journal of Graphic Technology, 14.

  13. Farsi M, Elhosseini MA, Badawy M, Arafat H, ZainEldin H (2019) Deployment techniques in wireless sensor networks, coverage and connectivity: a survey. IEEE Access.

  14. Gupta SK, Kuila P, Jana PK (2016) Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput Electr Eng 56:544–556.

    Article  Google Scholar 

  15. Hanh NT, Binh HTT, Hoai NX, Palaniswami MS (2019) An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Inf Sci 488:58–75.

    MathSciNet  Article  Google Scholar 

  16. Jha SK, Eyong EM (2018) An energy optimization in wireless sensor networks by using genetic algorithm. Telecommun Syst 67(1):113–121.

    Article  Google Scholar 

  17. Kalayci TE, Yildirim KS, Ugur A (2007) Maximizing coverage in a connected and k-covered wireless sensor network using genetic algorithms. Int J Appl Math Inf 1(3):123–130.

    Article  Google Scholar 

  18. Khoufi I, Minet P, Laouiti A, Mahfoudh S (2016) Survey of deployment algorithms in wireless sensor networks: coverage and connectivity issues and challenges. Int J Auton Adapt Commun Syst (IJAACS) 10(4):341–390.

    Article  Google Scholar 

  19. Kramer O (2017) Genetic algorithms. In: Genetic algorithm essentials, pp 11–19. Springer.

  20. Mahamuni CV (2016) A military surveillance system based on wireless sensor networks with extended coverage life. In: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp 375–381.

  21. Mnasri S, Thaljaoui A, Nasri N, Val T (2015) A genetic algorithm-based approach to optimize the coverage and the localization in the wireless audio-sensors networks. In: Networks, Computers and Communications (ISNCC), 2015 International Symposium on IEEE, pp 1–6.

  22. Moh’d Alia O, Al-Ajouri A (2017) Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sens J 17(3):882–896.

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Mostafaei H, Montieri A, Persico V, Pescapé A (2016) An efficient partial coverage algorithm for wireless sensor networks. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp 501–506.

  25. Mostafaei H, Obaidat MS (2017) A greedy overlap-based algorithm for partial coverage of heterogeneous wsns. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp 1–6.

  26. Musa A, Gonzalez V, Barragan D (2019) A new strategy to optimize the sensors placement in wireless sensor networks. J Ambient Intell Humaniz Comput 10(4):1389–1399.

    Article  Google Scholar 

  27. Nehra V, Sharma AK, Tripathi RK (2019) I-deec: improved deec for blanket coverage in heterogeneous wireless sensor networks. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  28. Özdağ R, CANAYAZ M (2017) A new dynamic deployment approach based on whale optimization algorithm in the optimization of coverage rates of wireless sensor networks. Eur J Technique 7(2):119–130.

    Article  Google Scholar 

  29. Öztürk C, Karaboğa D, GÖRKEMLİ B (2012) Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turkish J Electr Eng Comput Sci 20(2):255–262.

    Article  Google Scholar 

  30. Priyadarshini RR, Sivakumar N (2019) Enhancing coverage and connectivity using energy prediction method in underwater acoustic wsn. J Ambient Intell Hum Comput.

    Article  Google Scholar 

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

    MathSciNet  Article  MATH  Google Scholar 

  32. Sengupta S, Das S, Nasir M, Panigrahi BK (2013) Multi-objective node deployment in wsns: in search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng Appl Artif Intell 26(1):405–416.

    Article  Google Scholar 

  33. Sharma V, Patel R, Bhadauria H, Prasad D (2016) Deployment schemes in wireless sensor network to achieve blanket coverage in large-scale open area: a review. Egypt Inf J 17(1):45–56.

    Article  Google Scholar 

  34. Singh A, Sharma T (2014) A survey on area coverage in wireless sensor networks. In: Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on IEEE, pp 829–836.

  35. Sivanandam S, Deepa S (2008) Genetic algorithms. In: Introduction to genetic algorithms, pp 15–37, Springer.

  36. Su S, Zhao S (2017) A hierarchical hybrid of genetic algorithm and particle swarm optimization for distributed clustering in large-scale wireless sensor networks. J Ambient Intell Hum Comput.

    Article  Google Scholar 

  37. Tian H (2019) Vigilnet:an integrated sensor network system for energy-efficient surveillance. [Online; accessed 8-July-2019]

  38. Tripathi A, Gupta HP, Dutta T, Mishra R, Shukla K, Jit S (2018) Coverage and connectivity in wsns: a survey, research issues and challenges. IEEE Access 6:26971–26992.

    Article  Google Scholar 

  39. Tuba E, Tuba M, Beko M (2017) Mobile wireless sensor networks coverage maximization by firefly algorithm. In: Radioelektronika (RADIOELEKTRONIKA), 2017 27th International Conference, pp 1–5.

  40. Wolpert DH, Macready WG et al (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82.

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Hanaa ZainEldin.

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

Verify currency and authenticity via CrossMark

Cite this article

ZainEldin, H., Badawy, M., Elhosseini, M. et al. An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks. J Ambient Intell Human Comput 11, 4177–4194 (2020).

Download citation


  • Coverage
  • Deployment techniques
  • Genetic algorithm (GA)
  • Whale optimization algorithm (WOA)
  • Wireless sensor network (WSN)
  • Quality of service (QoS)