Skip to main content
SpringerLink
Log in

Review of Evolutionary Algorithms for Energy Efficient and Secure Wireless Sensor Networks

  • Conference paper
  • First Online:
Cyber Security and Digital Forensics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 73))

Abstract

Wireless sensor network (WSN) finds vast real-world applications in the field of energy control, security, health care, defense, and environment monitoring. WSNs are subdued by limited power with a specific battery backup. Due to the large distance between sensor nodes and sink, more consumption of power takes place in the sensors. Limited energy of sensor nodes is a major drawback to empower a large network coverage area. Therefore, the battery life and location of cluster heads play an important role in increasing the efficiency and lifetime of sensor nodes for long-term operation in WSNs. While there are many algorithms leading to the optimization of performance using convergence, comparison of such algorithms and their advantages and challenges is addressed. Different types of attacks and security goals are described for high-level security and privacy in WSNs. This paper tabulates a systematic survey of the evolutionary algorithms of WSNs based on nature. This paper also intends to reflect on the security challenges of WSN and proposes effective techniques to address them.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, X., He, D.: Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. J. Netw. Comput. Appl. 39, 310–318 (2014). https://doi.org/10.1016/j.jnca.2013.07.010

    Article  Google Scholar 

  2. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proc. Annu. Hawaii Int. Conf. Syst. Sci. 2000-Janua, 1–10 (2000)

    Google Scholar 

  3. Gupta, I., Riordan, D., Sampalli, S.: Cluster-head election using fuzzy logic for wireless sensor networks. In: Proc. 3rd Annu. Commun. Networks Serv. Res. Conf. 2005, pp. 255–260 (2005). https://doi.org/10.1109/CNSR.2005.27

  4. Tamandani, Y.K., Bokhari, M.U.: SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network. Wirel. Netw. 22, 647–653 (2016). https://doi.org/10.1007/s11276-015-0997-x

    Article  Google Scholar 

  5. AbdulAlim, M.A., Wu, Y.C., Wang, W.: A fuzzy based clustering protocol for energy-efficient wireless sensor networks. Adv. Mater. Res. 760–762, 685–690 (2013). https://doi.org/10.4028/www.scientific.net/AMR.760-762.685

  6. Gupta, S.K., Kuila, P., Jana, P.K.: GAR: an energy efficient GA-based routing for wireless sensor networks. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 7753 LNCS, pp. 267–277 (2013). https://doi.org/10.1007/978-3-642-36071-8_21

  7. Kuila, P., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach (2014)

    Google Scholar 

  8. Blum, C., López-Ibáñez, M.: Ant colony optimization. Intell. Syst. (2016). https://doi.org/10.4249/scholarpedia.1461

  9. Kaushik, A., Indu, S., Gupta, D.: Optimizing and enhancing the lifetime of a wireless sensor network using biogeography based optimization. Commun. Comput. Inf. Sci. 899, 260–272 (2019). https://doi.org/10.1007/978-981-13-2035-4_23

    Article  Google Scholar 

  10. Zhang, S., Xu, S., Zhang, W., Yu, D., Chen, K.: A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing 272, 439–452 (2018). https://doi.org/10.1016/j.neucom.2017.07.011

    Article  Google Scholar 

  11. Shukla, K., Verma, M., Gupta, D.: Age-Structured Biogeography-based Optimization, pp. 339–346 (2020)

    Google Scholar 

  12. Kaushik, A., Indu, S., Gupta, D.: A grey wolf optimization approach for improving the performance of wireless sensor networks. Wirel. Pers. Commun. 1429–1449 (2019). https://doi.org/10.1007/s11277-019-06223-2

  13. Low, C.P., Fang, C., Ng, J.M., Ang, Y.H.: Efficient load-balanced clustering algorithms for wireless sensor networks. Comput. Commun. 31, 750–759 (2008). https://doi.org/10.1016/j.comcom.2007.10.020

    Article  Google Scholar 

  14. He, J., Ji, S., Yan, M., Pan, Y., Li, Y.: Load-balanced CDS construction in wireless sensor networks via genetic algorithm. Int. J. Sens. Netw. 11, 166–178 (2012). https://doi.org/10.1504/IJSNET.2012.046331

    Article  Google Scholar 

  15. Raha, A., Kanti Naskar, M., Paul, A., Chakraborty, A., Karmakar, A.: A genetic algorithm inspired load balancing protocol for congestion control in wireless sensor networks using trust based routing framework (GACCTR). Int. J. Comput. Netw. Inf. Secur. 5, 9–20 (2013). https://doi.org/10.5815/ijcnis.2013.09.02

    Article  Google Scholar 

  16. Balaji, S., Julie, E.G., Rajaram, M., Robinson, Y.H.: Fuzzy based particle swarm optimization routing technique for load balancing in wireless sensor networks. Int. J. Comput. Inf. Eng. 10, 1418–1427 (2016)

    Google Scholar 

  17. Liu, X., Qiu, T., Wang, T.: Load-balanced data dissemination for wireless sensor networks: a nature-inspired approach. IEEE Internet Things J. 6, 9256–9265 (2019). https://doi.org/10.1109/JIOT.2019.2900763

    Article  Google Scholar 

  18. Kaushik, A., Indu, S., Gupta, D.: A novel load balanced energy conservation approach in WSN using biogeography based optimization. AIP Conf. Proc. 1884, 1–4 (2017). https://doi.org/10.1063/1.5002507

    Article  Google Scholar 

  19. Bozorg-Haddad, O.: Studies in Computational Intelligence—Advanced Optimization by Nature-Inspired Algorithms (2018)

    Google Scholar 

  20. Lghd, V., Wkh, D., Fkdoohqjhv, I., Dqg, J., Glvdvwhu, F., Lqirupdwlrq, E., Dqg, F., Lpsruwdqfh, P., Wkh, V.R., Fkdudfwhuv, X., Wkhvh, R.I., Wkh, D.Q.G., Sxusrvhv, D., Xvhg, D.U.H., Surylgh, W.R., Iru, W., Dqg, L., Dwwdfnv, R., Wkhuh, Q.W., Whupv, D.U.H., Whup, Q., Xvhg, L. V, Wkh, D.L., Wkh, S.: $ 6xuyh\ rq 6hfxulw\ $wwdfnv lq :luhohvv 6hqvru 1hwzrunv. 536–539 (2016)

    Google Scholar 

  21. Wang, Q., Zhang, T.: A survey on security in wireless sensor networks. Secur. RFID Sens. Networks. 293–320 (2016). https://doi.org/10.5121/ijnsa.2017.9103

  22. Karakaya, A., Akleylek, S.: A survey on security threats and authentication approaches in wireless sensor networks. In: 6th Int. Symp. Digit. Forensic Secur. ISDFS 2018—Proceeding. 2018-Janua, 1–4 (2018). https://doi.org/10.1109/ISDFS.2018.8355381

  23. Teymourzadeh, M., Vahed, R., Alibeygi, S., Dastanpor, N.: Security in Wireless Sensor Networks: Issues and Challenges. arXiv (2020)

    Google Scholar 

  24. Perrig, A., Szewczyk, R., Tygar, J.D., Wen, V., Culler, D.E.: SPINS: security protocols for sensor networks. Wirel. Netw. 8, 521–534 (2002). https://doi.org/10.1023/A:1016598314198

    Article  MATH  Google Scholar 

  25. Conti, M., Di Pietro, R., Mancini, L., Mei, A.: Distributed detection of clone attacks in wireless sensor networks. IEEE Trans. Dependable Secur. Comput. 8, 685–698 (2011). https://doi.org/10.1109/TDSC.2010.25

    Article  Google Scholar 

  26. Gligor, V.D.: Emergent properties in ad-hoc networks: a security perspective. In: Proc. 2006 ACM Symp. Information, Comput. Commun. Secur. ASIACCS ’06. 2006, 1 (2006). https://doi.org/10.1145/1128817.1128819

  27. Ren, J., Zhang, Y., Zhang, K., Shen, X.: Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Trans. Wirel. Commun. 15, 3718–3731 (2016). https://doi.org/10.1109/TWC.2016.2526601

    Article  Google Scholar 

  28. Di Pietro, R., Mancini, L. V., Law, Y.W., Etalle, S., Havinga, P.: LKHW: a directed diffusion-based secure multicast scheme for wireless sensor networks. In: Proc. Int. Conf. Parallel Process. Work. 2003-Janua, pp. 397–406 (2003). https://doi.org/10.1109/ICPPW.2003.1240395

  29. Seo, S.H., Won, J., Sultana, S., Bertino, E.: Effective key management in dynamic wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 10, 371–383 (2015). https://doi.org/10.1109/TIFS.2014.2375555

    Article  Google Scholar 

  30. Dong, M., Ota, K., Yang, L.T., Liu, A., Guo, M.: LSCD: a low-storage clone detection protocol for cyber-physical systems. IEEE Trans. Comput. Des. Integr. Circuits Syst. 35, 712–723 (2016). https://doi.org/10.1109/TCAD.2016.2539327

  31. Bhatia, T., Kansal, S., Goel, S., Verma, A.K.: A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Comput. Electr. Eng. 56, 441–455 (2016). https://doi.org/10.1016/j.compeleceng.2016.09.016

    Article  Google Scholar 

  32. Gupta, S.K., Kuila, P., Jana, P.K.: Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput. Electr. Eng. 56, 544–556 (2016). https://doi.org/10.1016/j.compeleceng.2015.11.009

    Article  Google Scholar 

  33. Shokouhifar, M., Jalali, A.: A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU Int. J. Electron. Commun. 69, 432–441 (2015). https://doi.org/10.1016/j.aeue.2014.10.023

    Article  Google Scholar 

  34. Khalesian, M., Delavar, M.R.: Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach. Eng. Appl. Artif. Intell. 53, 126–139 (2016). https://doi.org/10.1016/j.engappai.2016.03.004

    Article  Google Scholar 

  35. Pal, V., Yogita, Singh, G., Yadav, R.P.: Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Comput. Sci. 57, 1417–1423 (2015). https://doi.org/10.1016/j.procs.2015.07.461

  36. Zhang, X.Y., Zhang, J., Gong, Y.J., Zhan, Z.H., Chen, W.N., Li, Y.: Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Trans. Evol. Comput. 20, 695–710 (2016). https://doi.org/10.1109/TEVC.2015.2511142

    Article  Google Scholar 

  37. Ye, M., Wang, Y., Dai, C., Wang, X.: A hybrid genetic algorithm for the minimum exposure path problem of wireless sensor networks based on a numerical functional extreme model. IEEE Trans. Veh. Technol. 65, 8644–8657 (2016). https://doi.org/10.1109/TVT.2015.2508504

    Article  Google Scholar 

  38. Kiranyaz, S.: Particle swarm optimization. Adapt. Learn. Optim. 15, 45–82 (2014). https://doi.org/10.1007/978-3-642-37846-1_3

  39. Taherian, M., Karimi, H., Kashkooli, A.M., Esfahanimehr, A., Jafta, T., Jafarabad, M.: The design of an optimal and secure routing model in wireless sensor networks by using PSO algorithm. Procedia Comput. Sci. 73, 468–473 (2015). https://doi.org/10.1016/j.procs.2015.12.028

    Article  Google Scholar 

  40. Guo, W., Li, J., Chen, G., Niu, Y., Chen, C.: A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 26, 3236–3249 (2015). https://doi.org/10.1109/TPDS.2014.2386343

    Article  Google Scholar 

  41. Rejinaparvin, J., Vasanthanayaki, C.: Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sens. J. 15, 4264–4274 (2015). https://doi.org/10.1109/JSEN.2015.2416208

    Article  Google Scholar 

  42. Yan, Z., Goswami, P., Mukherjee, A., Yang, L., Routray, S., Palai, G.: Low-energy PSO-based node positioning in optical wireless sensor networks. Optik (Stuttg). 181, 378–382 (2019). https://doi.org/10.1016/j.ijleo.2018.12.055

    Article  Google Scholar 

  43. Wang, Y.L., Song, M., Wei, Y.F., Wang, Y.H., Wang, X.J.: Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks. J. China Univ. Posts Telecommun. 21, 43–53 (2014). https://doi.org/10.1016/S1005-8885(14)60267-3

    Article  Google Scholar 

  44. Sun, Z., Wei, M., Zhang, Z., Qu, G.: Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Appl. Soft Comput. J. 77, 366–375 (2019). https://doi.org/10.1016/j.asoc.2019.01.034

    Article  Google Scholar 

  45. Kaushik, A., Indu, S., Gupta, D.: Adaptive mobile sink for energy efficient WSN using biogeography-based optimization. Int. J. Mob. Comput. Multimed. Commun. 10, 1–22 (2019). https://doi.org/10.4018/IJMCMC.2019070101

    Article  Google Scholar 

  46. Hatta, N.M., Zain, A.M., Sallehuddin, R., Shayfull, Z., Yusoff, Y.: Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artif. Intell. Rev. 52, 2651–2683 (2019). https://doi.org/10.1007/s10462-018-9634-2

    Article  Google Scholar 

  47. Al-Aboody, N.A., Al-Raweshidy, H.S.: Grey Wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks. In: 2016 4th Int. Symp. Comput. Bus. Intell. ISCBI 2016, pp. 101–107 (2016). https://doi.org/10.1109/ISCBI.2016.7743266

  48. Balasubramanian, D., Govindasamy, V.: Study on evolutionary approaches for improving the energy efficiency of wireless sensor networks applications. EAI Endorsed Trans. Internet Things. 5, 164856 (2020). https://doi.org/10.4108/eai.13-7-2018.164856

  49. Rajakumar, R., Amudhavel, J., Dhavachelvan, P., Vengattaraman, T.: GWO-LPWSN: grey wolf optimization algorithm for node localization problem in wireless sensor networks. J. Comput. Networks Commun. 2017 (2017). https://doi.org/10.1155/2017/7348141

  50. Sharawi, M., Emary, E.: Impact of grey Wolf optimization on WSN cluster formation and lifetime expansion. In: 9th Int. Conf. Adv. Comput. Intell. ICACI 2017, pp. 157–162 (2017). https://doi.org/10.1109/ICACI.2017.7974501

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Delhi Technological University, Delhi, India

    Rajiv Yadav & S. Indu

  2. Department of CSE, Delhi Technological University, Delhi, India

    Daya Gupta

Authors
  1. Rajiv Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Indu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daya Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The NorthCap University, Gurugram, India

    Kavita Khanna

  2. Departamento of Telecommunications (TET), Universidade Federal Fluminese, Duque de Caxias, Rio de Janeiro, Brazil

    Vania Vieira Estrela

  3. Av. Ministro Petrônio Portela, Universidade Federal Do Piauí, Teresina, Piauí, Brazil

    Joel José Puga Coelho Rodrigues

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yadav, R., Indu, S., Gupta, D. (2022). Review of Evolutionary Algorithms for Energy Efficient and Secure Wireless Sensor Networks. In: Khanna, K., Estrela, V.V., Rodrigues, J.J.P.C. (eds) Cyber Security and Digital Forensics . Lecture Notes on Data Engineering and Communications Technologies, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-16-3961-6_49

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3961-6_49

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3960-9

  • Online ISBN: 978-981-16-3961-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation