Skip to main content

Advertisement

SpringerLink
Log in

Simultaneous sensor and relay nodes deployment for Smart Car Park surveillance

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

This study aims to optimize the deployment of wireless sensor networks for Smart Car Parks surveillance applications. Our proposed approach involves simultaneously deploying Sensor Nodes (SNs) and Relay Nodes, which we argue is more efficient than existing sequential placement methods. Our approach identifies optimal positions for both types of nodes to minimize their numbers and the network diameter while ensuring the coverage of each target point by at least K SNs and network connectivity constraints. To accomplish this, we have developed a Multi-Objective Linear Programming model (MOLP) to solve small instances of the deployment problem. Still, for large instances, we have introduced a novel algorithm called the Greedy Chaos Whale Optimization meta-heuristic (GCWOA) which is mainly based on: a Greedy algorithm to generate initial random nodes positions while satisfying the problem’s constraints and a chaos local search (CLS) technique using chaos maps to decide whether to include a given node in the final solution. The solution population was divided into two sub-populations, with the first undergoing CLS and the second undergoing the Whale Optimization Algorithm (WOA) to optimize the solution further. We have compared the performance of our proposed GCWOA with that of MOLP, and well-known existing meta-heuristics, including WOA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Our results reveal that the average fitness of the GCWOA is close to optimal and lower by 23.45%, 28.75%, and 26.32% than basic WOA, GA, and PSO, respectively. The GCWOA exhibits faster convergence and a lower running time than the basic WOA, GA, and PSO.

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

Similar content being viewed by others

References

  1. Abdel-Basset M, Mohamed R, Mirjalili S (2021) A novel whale optimization algorithm integrated with Nelder-mead simplex for multi-objective optimization problems. Knowl-Based Syst 212:106619

    Article  Google Scholar 

  2. Akyol S, Alatas B (2017) Plant intelligence based metaheuristic optimization algorithms. Artif Intell Rev 47:417–462

    Article  Google Scholar 

  3. Bagaa M, Chelli A, Djenouri D, Taleb T, Balasingham I, Kansanen K (2017) Optimal placement of relay nodes over limited positions in wireless sensor networks. IEEE Trans Wireless Commun 16(4):2205–2219

    Article  Google Scholar 

  4. Becerra RL, Coello CAC (2006) Solving hard multiobjective optimization problems using \(\varepsilon\)-constraint with cultured differential evolution. In: Parallel problem solving from nature-PPSN IX. Springer, pp. 543–552

  5. Benghelima SC, Ould-Khaoua M, Benzerbadj A, Baala O (2021) Multi-objective optimisation of wireless sensor networks deployment: application to fire surveillance in smart car parks. In: 2021 international wireless communications and mobile computing (IWCMC), pp 98–104

  6. Benghelima SC, Ould-Khaoua M, Benzerbadj A, Baala O, Ben-Othman J (2022) Optimization of the deployment of wireless sensor networks dedicated to fire detection in smart car parks using chaos whale optimization algorithm. In: ICC 2022-IEEE international conference on communications. IEEE, pp 3592–3597

  7. Bingol H, Alatas B (2016) Chaotic league championship algorithms. Arab J Sci Eng 41:5123–5147

    Article  MathSciNet  MATH  Google Scholar 

  8. Boukerche A, Sun P (2018) Connectivity and coverage based protocols for wireless sensor networks. Ad Hoc Netw 80:54–69

    Article  Google Scholar 

  9. Bundy A, Wallen L (1984) Breadth-first search. In: Catalogue of artificial intelligence tools. Springer, Berlin, pp 13–13

  10. Chelbi S, Dhahri H, Bouaziz R (2021) Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks. Int J Commun Syst 34(9):e4813

    Article  Google Scholar 

  11. Gharehchopogh S, Farhad A, Benyamin AB (2023) An improved farmland fertility algorithm with hyper-heuristic approach for solving travelling salesman problem. Comput Model Eng Sci 135(3):1981–2006

    Google Scholar 

  12. Tang R, Fong S, Dey N (2018) Metaheuristics and chaos theory. In: Mohamedamen KA, Naimee A (eds) Chaos theory, chapter 10. IntechOpen, Rijeka

    Google Scholar 

  13. Deepa R, Revathi V (2021) Enhancing whale optimization algorithm with levy flight for coverage optimization in wireless sensor networks. Comput Electr Eng 94:107359

    Article  Google Scholar 

  14. Du P, Cheng W, Liu N, Zhang H, Lu J (2020) A modified whale optimization algorithm with single-dimensional swimming for global optimization problems. Symmetry 12(11)

  15. Farsi M, Elhosseini MA, Badawy M, Ali HA, Eldin HZ (2019) Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access 7:28940–28954

    Article  Google Scholar 

  16. Gharehchopogh FS (2022) An improved Harris Hawks optimization algorithm with multi-strategy for community detection in social network. J Bionic Eng 1–23

  17. Gupta GP, Jha S (2019) Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wireless Netw 25(6):3167–3177

    Article  Google Scholar 

  18. 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 544–556

  19. Haq MZU, Khan MZ, Rehman HU, Mehmood G, Binmahfoudh A, Krichen M, Alroobaea R (2022) An adaptive topology management scheme to maintain network connectivity in wireless sensor networks. Sensors 22(8):2855

    Article  Google Scholar 

  20. Jaiswal K, Anand V (2021) A QoS aware optimal node deployment in wireless sensor network using grey wolf optimization approach for IoT applications. Telecommun Syst 78(4):559–576

    Article  Google Scholar 

  21. Kaur G, Gupta SH, Kaur H (2022) Performance evaluation and optimization of long range iot network using whale optimization algorithm. Cluster Comput 1–15

  22. Liang W, Ma C, Zheng M, Luo L (2019) Relay node placement in wireless sensor networks: from theory to practice. IEEE Trans Mob Comput 20(4):1602–1613

    Article  Google Scholar 

  23. Lin T, Rivano H, Le Mouël F (2017) A survey of smart parking solutions. IEEE Trans Intell Transp Syst 18(12):3229–3253

    Article  Google Scholar 

  24. Liu X (2015) A deployment strategy for multiple types of requirements in wireless sensor networks. IEEE Trans Cybernet 45(10):2364–2376

    Article  Google Scholar 

  25. Ma C, Liang W, Zheng M (2017) Delay constrained relay node placement in two-tiered wireless sensor networks: a set-covering-based algorithm. J Netw Comput Appl 93:76–90

    Article  Google Scholar 

  26. Ma C, Liang W, Zheng M, Yang B (2018) Relay node placement in wireless sensor networks with respect to delay and reliability requirements. IEEE Syst J 13(3):2570–2581

    Article  Google Scholar 

  27. Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862

    Article  MathSciNet  MATH  Google Scholar 

  28. Marler RT, Arora JS (2005) Function-transformation methods for multi-objective optimization. Eng Optim 37(6):551–570

    Article  MathSciNet  Google Scholar 

  29. Matin MA, Islam MM (2012) Overview of wireless sensor network. In: Wireless sensor networks-technology and protocols, pp 1–3

  30. Megiddo N, Supowit KJ (1984) On the complexity of some common geometric location problems. SIAM J Comput 13(1):182–196

    Article  MathSciNet  MATH  Google Scholar 

  31. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  32. Mohammadzadeh H, Gharehchopogh FS (2021) A multi-agent system based for solving high-dimensional optimization problems: a case study on email spam detection. Int J Commun Syst 34(3):e4670

    Article  Google Scholar 

  33. Chandra N, Pushparaj SD (2021) Optimal sensors placement scheme for targets coverage with minimized interference using BBO. Evolut Intell 1–15

  34. Ndam NA, Abba AAA, Nana AM, Chafiq T, Nabila L, Yves EJ, Wahabou A, Abdelhak G (2020) Hybrid wireless sensors deployment scheme with connectivity and coverage maintaining in wireless sensor networks. Wirel Pers Commun 112(3):1893–1917

    Article  Google Scholar 

  35. Rao AN, Naik BR, Devi LN (2020) On the relay node placement in WSNs for lifetime maximization through metaheuristics. Mater Today Proc

  36. 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 Oper Res 59:11–21

    Article  MathSciNet  MATH  Google Scholar 

  37. Mrutyunjay R, Rajarshi R (2017) Optimal wireless sensor network information coverage using particle swarm optimisation method. Int J Electron Lett 5(4):491–499

    Article  Google Scholar 

  38. Bonab MS, Ghaffari A, Gharehchopogh FS, Alemi P (2020) A wrapper-based feature selection for improving performance of intrusion detection systems. Int J Commun Syst 33(12):e4434

    Article  Google Scholar 

  39. Sapre S, Mini S (2018) Optimized relay nodes positioning to achieve full connectivity in wireless sensor networks. Wireless Pers Commun 99(4):1521–1540

    Article  Google Scholar 

  40. Shishavan ST, Gharehchopogh FS (2022) An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimedia Tools Appl 81(18):25205–25231

    Article  Google Scholar 

  41. Sun Y, Halgamuge S (2019) Minimum-cost heterogeneous node placement in wireless sensor networks. IEEE Access 7:14847–14858

    Article  Google Scholar 

  42. Toloueiashtian M, Golsorkhtabaramiri M, Rad SYB (2022) An improved whale optimization algorithm solving the point coverage problem in wireless sensor networks. Telecommun Syst 79(3):417–436

    Article  Google Scholar 

  43. Tsai C-W, Tsai P-W, Pan J-S, Chao H-C (2015) Metaheuristics for the deployment problem of WSN: a review. Microprocess Microsyst 39(8):1305–1317

    Article  Google Scholar 

  44. Altay EV, Alatas B (2020) Bird swarm algorithms with chaotic mapping. Artif Intell Rev 53:1373–1414

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LRDSI, Department of Computer Science, University of Blida 1, Route de Soumaa, Blida, Algeria

    Slimane Charafeddine Benghelima & Mohamed Ould Khaoua

  2. Department of Mathematics and Computer Science, University of Ain Temouchent, Ain Temouchent, Algeria

    Ali Benzerbadj

  3. RIIR Laboratory, University of Oran 1 Ahmed Ben Bella, Oran, Algeria

    Ali Benzerbadj

  4. UTBM, CNRS, Institut FEMTO-ST, F-90010, Belfort cedex, France

    Oumaya Baala

Authors
  1. Slimane Charafeddine Benghelima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ould Khaoua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Benzerbadj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oumaya Baala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Slimane Charafeddine Benghelima.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest in this paper.

Additional information

Publisher's Note

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

Appendix

Appendix

Table 8 displays the values of the weights (\(\alpha\), \(\beta\), \(\gamma\)) and their achieved number of SNs, RNs and the ND, while the bold lines indicate the non-dominated solutions.

Table 8 Values for the weights \(\alpha , \beta\) and \(\gamma\) and their corresponding achieved objectives in terms of the number of SNs, RNs, and ND
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benghelima, S.C., Ould Khaoua, M., Benzerbadj, A. et al. Simultaneous sensor and relay nodes deployment for Smart Car Park surveillance. Evol. Intel. (2023). https://doi.org/10.1007/s12065-023-00853-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12065-023-00853-z

Keywords

Search

Navigation