Skip to main content
SpringerLink
Log in

An enhanced architecture for route discovery and load balancing in WSN

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The sensor network comprises various sensor nodes in which packet transfer requires detection of the route. The discovery of the path in such a network becomes cumbersome. Thus, to detect an appropriate route, a robust mechanism is incorporated into this novel research. A three-block architecture such as 3B-RLLM has been developed, which discovers the route, calculates the lifetime, and balances the load in the network. 3B-RLLM uses Lagrange’s interpolation to check the security. In addition, the proposed architecture computes the distance between the nodes using the Cosine similarity measure. The detected measure determines the distance between the sensor nodes to transfer the packets. The transmitted packets have been evaluated using the QoS (Quality of Service) parameter. The different parameters have been considered, such as throughput, Delay, and Lifetime to compute the effectiveness of the proposed architecture. The calculated parameters have been compared with past research to determine the efficacy of the developed model. The proposed architecture provides better results than past research.

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. Bhushan B, Sahoo G (2019) Routing protocols in wireless sensor networks. In: Computational intelligence in sensor networks, Springer, Berlin, pp. 215–248

  2. García VL, Sandoval OA, Trivino CA, Abbas CB (2009) Routing protocols in wireless sensor networks. Sensors 9(11):8399–8421

    Article  Google Scholar 

  3. Randhawa S, Jain S (2019) MLBC: Multi-objective load balancing clustering technique in wireless sensor networks. Appl Soft Comput 74:66–68

    Article  Google Scholar 

  4. Sarkar A, Murugan TS (2019) Cluster head selection for energy-efficient and delay-less routing in a wireless sensor network. Wireless Netw 25(1):303–320

    Article  Google Scholar 

  5. Sabor N, Sasaki S, Abo-Zahid M, Ahmed SM (2017) A comprehensive survey on hierarchical-based routing protocols for mobile wireless sensor networks: review, taxonomy, and future directions. In: Wireless communications and mobile computing

  6. Shen H, Li Z (2015) A P2P-based market-guided distributed routing mechanism for high-throughput hybrid wireless networks. IEEE Trans Mob Comput 14:245–260

    Article  Google Scholar 

  7. Yang Y, Fonoage MI, Cardei M (2010) Improving network lifetime with mobile wireless sensor networks. Comput Commun 33(4):409–419

    Article  Google Scholar 

  8. Banerjee T, Xie B, Jun JH, Agrawal DP (2010) Increasing lifetime of wireless sensor networks using controllable mobile cluster heads. Wirel Commun Mob Comput 10(3):313–336

    Google Scholar 

  9. Prakash S, Saroj V (2019) A review of wireless charging nodes in wireless sensor networks. Data science and big data analytics. Springer, Singapore, pp 177–188

    Chapter  Google Scholar 

  10. Ju X, Liu W, Zhang C, Liu A, Wang T, Xiong N, Cai Z (2018) An energy conserving and transmission radius adaptive scheme to optimize performance of energy harvesting sensor networks. Sensors 18(9):2885

    Article  Google Scholar 

  11. Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645

    Article  Google Scholar 

  12. Rakhee SMB (2016) Cluster based energy efficient routing protocol using ANT colony optimization and breadth first search. Procedia Comp Sci 89:124–133

    Article  Google Scholar 

  13. Hu X, Li Y, Xu H (2017) Multi-mode clustering model for hierarchical wireless sensor networks. Phys A Stat Mech Appl 469:665–675

    Article  Google Scholar 

  14. Chung-Shuo FAN (2013) Rich: Region-based intelligent cluster-head selection and node deployment strategy in concentric-based WSNs. Adv Elect Computer Eng 13(4):3–8

    Article  Google Scholar 

  15. Vivekchandran KC, Nikesh Narayan P (2015) Energy efficiency and latency improving in wireless sensor networks. Int J Sci Res (IJSR) 4(5):1291–1295

    Google Scholar 

  16. Soares SM, Carvalho MM (2019) Throughput analytical modeling of ieee 802.11 in wireless networks. In 16th IEEE Annual Consumer Communications & Networking Conference (CCNC, 2019) (pp. 1–4). IEEE

  17. Liu Y, Liu A, Zhang N, Liu X, Ma M, Hu Y (2019) DDC: Dynamic duty cycle for improving delay and energy efficiency in wireless sensor networks. J Netw Comput Appl 131:16–27

    Article  Google Scholar 

  18. Chanak P, Banerjee I, Rahaman H (2015) Load management scheme for energy holes reduction in wireless sensor networks. Comput Electr Eng 48:343–357

    Article  Google Scholar 

  19. Santiago S, Kumar ADV, Arockiam L (2018) EALBA: energy aware load balancing algorithm for IoT networks. In: Proceedings of the 2018 international conference on mechatronic systems and robots, pp 46–50

  20. Gu Y, Ren F, Ji Y, Li J (2015) The evolution of sink mobility management in wireless sensor networks: a survey. IEEE Commun Surv Tutorials 18(1):507–524

    Article  Google Scholar 

  21. Yang C, Liu C, Zhang X, Nepal S, Chen J (2014) A time efficient approach for detecting errors in big sensor data on cloud. IEEE Trans Parallel Distrib Syst 26(2):329–339

    Article  Google Scholar 

  22. Ahmed AM, Paulus R (2017) Congestion detection technique for multipath routing and load balancing in WSN. Wireless Netw 23(3):881–888

    Article  Google Scholar 

  23. Li X, Keegan B, Mtenzi F, Weise T, Tan M (2019) Energy-efficient load balancing ant based routing algorithm for wireless sensor networks. IEEE Access 7:113182–113196

    Article  Google Scholar 

  24. Sampathkumar A, Mulerikkal J, Sivaram M (2020) Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wireless Networks 1–12.

  25. Lipare A, Edla DR, Kuppili V (2019) Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function. Appl Soft Comput 84:105706

    Article  Google Scholar 

  26. Barzin A, Sadegheih A, Zare HK, Honarvar M (2020) A hybrid swarm intelligence algorithm for clustering-based routing in wireless sensor networks. J Circu Syst Comput 29(10):2050163

    Article  Google Scholar 

  27. Dattatraya KN, Rao KR (2019) Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. Journal of King Saud University Computer and Information Sciences

  28. Agrawal D, Pandey S (2020) Load balanced fuzzy-based clustering for WSNs. International conference on innovative computing and communications. Springer, Singapore, pp 583–592

    Chapter  Google Scholar 

  29. Chen B, Yao N, Liu W, Liu J, Li X, Hao X (2019) Distributed topology control algorithm based on load balancing evaluation model in wireless sensor networks. Wireless Pers Commun 109(4):2607–2625

    Article  Google Scholar 

  30. Faheem M, Gungor VC (2018) Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0. Appl Soft Comput 68:910–922

    Article  Google Scholar 

  31. Cheng L, Niu J, Cao J, Das SK, Gu Y (2014) QoS aware geographic opportunistic routing in wireless sensor networks. IEEE Trans Parallel Distrib Syst 25(7):1864–1875

    Article  Google Scholar 

  32. Pal V, Singh G, Yadav RP (2015) Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Comput Sci 57:1417–1423

    Article  Google Scholar 

  33. RejinaParvin J, Vasanthanayaki C (2015) Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sens J 15(8):4264–4274

    Article  Google Scholar 

  34. .Jia D, Zhu H, Zou S, Hu P, (2015) Dynamic cluster head selection method for wireless sensor network. IEEE Sens J 16(8):2746–2754

    Google Scholar 

  35. Baskaran M, Sadagopan C (2015) Synchronous firefly algorithm for cluster head selection in WSN. Sci World J 1–7, EEE. (2019, January)

  36. Kaur M, Sohi BS (2018) Comparative analysis of bio inspired optimization techniques in wireless sensor networks with GA PSO approach. Ind J Sci Technol 11(4):1–10

    Article  Google Scholar 

  37. Sun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320

    Article  Google Scholar 

  38. Zungeru AM, Ang LM, Seng KP (2012) Classical and swarm intelligence based routing protocols for wireless sensor networks: a survey and comparison. J Netw Comput Appl 35(5):1508–1536

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IK Gujral Punjab Technical University, Jalandhar, Punjab, India

    Mandeep Kaur

  2. Department of ECE, IKG PTU Main Campus, Kapurthala, Punjab, India

    Amit Gupta

  3. CGC Group of Colleges, Mohali, Punjab, India

    Balwinder Singh Sohi

Authors
  1. Mandeep Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amit Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Balwinder Singh Sohi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mandeep Kaur.

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

Kaur, M., Gupta, A. & Sohi, B.S. An enhanced architecture for route discovery and load balancing in WSN. J Supercomput 77, 12609–12629 (2021). https://doi.org/10.1007/s11227-021-03777-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03777-6

Keywords

Search

Navigation