Skip to main content
SpringerLink
Log in

Enhancing the Network Performance of Wireless Sensor Networks on Meta-heuristic Approach: Grey Wolf Optimization

  • Conference paper
  • First Online:
Applications of Artificial Intelligence and Machine Learning

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 778))

Abstract

The sensing technology has brought all advancements in the human lives. Wireless sensor network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In this paper, we have presented a comparative evaluation of state of art meta-heuristic approaches that helps in acquiring energy efficiency in the network. We have proposed grey wolf optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select the appropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy and so on.

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
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Google Scholar 

  2. Arampatzis T, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and wireless sensor networks. In: Proceedings of the 2005 IEEE international symposium on intelligent control. Mediterranean conference on control and automation. IEEE, pp 719–724. Retrieved from http://ieeexplore.ieee.org/abstract/document/1467103/

  3. Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14):2826–2841

    Google Scholar 

  4. Sahoo RR, Singh M, Sahoo BM, Majumder K, Ray S, Sarkar SK (2013) A light weight trust based secure and energy efficient clustering in wireless sensor network: honey bee mating intelligence approach. Procedia Technol 10:515–523

    Article  Google Scholar 

  5. Sahoo BM, Gupta AD, Yadav SA, Gupta S (2019, April) ESRA: enhanced stable routing algorithm for heterogeneous wireless sensor networks. In: 2019 International conference on automation, computational and technology management (ICACTM). IEEE, pp 148–152

    Google Scholar 

  6. Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3(3):325–349

    Google Scholar 

  7. Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  8. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  9. Gharaei N, Bakar KA, Hashim SZM, Pourasl AH (2019) Inter-and intra-cluster movement of mobile sink algorithms for cluster-based networks to enhance the network lifetime. Ad Hoc Netw 85:60–70

    Article  Google Scholar 

  10. Nayak P, Vathasavai B (2017, January) Genetic algorithm-based clustering approach for wireless sensor network to optimize routing techniques. In: 2017 7th International conference on cloud computing, data science & engineering-confluence. IEEE, pp 373–380

    Google Scholar 

  11. Ahmed MM, Houssein EH, Hassanien AE, Taha A, Hassanien E (2017, September) Maximizing lifetime of wireless sensor networks based on whale optimization algorithm. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 724–733

    Google Scholar 

  12. Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw 102237

    Google Scholar 

  13. Kaur T, Kumar D (2018) Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks. IEEE Sens J 18(11):4614–4622

    Article  Google Scholar 

  14. Latiff NAA, Latiff NMAA, Ahmad RB (2011, April) Prolonging lifetime of wireless sensor networks with mobile base station using particle swarm optimization. In: 2011 Fourth international conference on modeling, simulation and applied optimization. IEEE, pp 1–6

    Google Scholar 

  15. Hu Y, Ding Y, Hao K, Ren L, Han H (2014) An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink. Int J Syst Sci 45(3):337–350

    Article  Google Scholar 

  16. Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56

    Article  Google Scholar 

  17. Gupta SK, Jana PK (2015) Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Pers Commun 83:2403–2423

    Article  Google Scholar 

  18. Bhola J, Soni S, Cheema GK (2020) Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. J Ambient Intell Humaniz Comput 11(3):1281–1288

    Article  Google Scholar 

  19. Arora VK, Sharma V, Sachdeva M (2019) ACO optimized self-organized tree-based energy balance algorithm for wireless sensor network. J Ambient Intell Humaniz Comput 10(12):4963–4975

    Article  Google Scholar 

  20. Sahoo BM, Rout RK, Umer S, Pandey HM (2020, January) ANT colony optimization based optimal path selection and data gathering in WSN. In: 2020 International conference on computation, automation and knowledge management (ICCAKM). IEEE, pp 113–119

    Google Scholar 

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

    Article  Google Scholar 

  22. Sahoo BM, Pandey HM, Amgoth T (2020) GAPSO-H: a hybrid approach towards optimizing the cluster-based routing in wireless sensor network. Swarm Evol Comput 100772

    Google Scholar 

  23. Visu P, Praba TS, Sivakumar N, Srinivasan R, Sethukarasi T (2020) Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks. J Ambient Intell Humaniz Comput 1–9

    Google Scholar 

  24. Senniappan V, Subramanian J (2018) Biogeography-based Krill Herd algorithm for energy efficient clustering in wireless sensor networks for structural health monitoring application. J Ambient Intell Smart Environ 10(1):83–93

    Article  Google Scholar 

  25. Saravanan D, Janakiraman S, Kalaipriyan T, Naresh MV (2018) An efficient routing model using Krill Herd optimization algorithm—a survey. Int J Pure Appl Math 119(14):377–383

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University Noida, Noida, Uttar Pradesh, India

    Biswa Mohan Sahoo

  2. Indian Institute of Technology (Indian School of Mines), Dhanbad, India

    Biswa Mohan Sahoo & Tarachand Amgoth

  3. Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, UK

    Hari Mohan Pandey

Authors
  1. Biswa Mohan Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarachand Amgoth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hari Mohan Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India

    Ankur Choudhary

  2. Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India

    Arun Prakash Agrawal

  3. Asia Pacific Centre for Analytics (APCA), Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia

    Rajasvaran Logeswaran

  4. Information Technology, University of South Florida Sarasota–Manatee Campus, Sarasota, FL, USA

    Bhuvan Unhelkar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 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

Sahoo, B.M., Amgoth, T., Pandey, H.M. (2021). Enhancing the Network Performance of Wireless Sensor Networks on Meta-heuristic Approach: Grey Wolf Optimization. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_35

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3067-5_35

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3066-8

  • Online ISBN: 978-981-16-3067-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation