Skip to main content

Advertisement

SpringerLink
Log in

Double attribute based node deployment in wireless sensor networks using novel weight based clustering approach

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

In recent years, WSNs have become one of the fastest emerging networks. It enables a larger variety of applications in the real-time as well as automation industries. WSN applications are made up of a large count of sensor nodes that are distributed as per the application's requirements. Sensor nodes, depending on its manufacturing rationale, monitor, sense, receive, record, and transfer any type of data. Sensors are inexpensive, tiny, and have limited energy efficiency. Inefficient methods of utilizing this scarce battery power results in the death of nodes which consequently affects the lifetime of the entire network. The failure of nodes because of inadequate routing strategies reduces the network's lifespan and overall quality. Numerous previous research methodologies were applied to improve network lifespan and node connection together with communication dependability. Most of the solutions failed to deliver ideal performance in terms of improving overall QoS, which is a collective characteristic. In this research, a novel WBC approach for data gathering, node clustering, and load balancing in WSN is proposed. The functioning of the proposed model relies on the effective assignment of nodes to the communication task based on their weighted function computed based on the performance characteristic. Load balancing as well as data aggregation, are the two attributes effectively considered in this research work. The performance of the suggested WBC is compared to traditional benchmark techniques using the NS2 program. Multiple measures have been calculated and studied, and in every case, the suggested WBC outperforms.

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

Figure 1
Figure 2

Similar content being viewed by others

Availability of Data & Material

The author hereby declare that no specific data sets are utilized in the proposed work.

Code availability:

Since, future works are based on the custom codes developed in this work, the code may not be available from the author.

Abbreviations

WSN:

Wireless Sensor Network

QoS:

Quality of Service

WBC:

Weight Based Clustering

CH:

Cluster Head

SFLA:

Shuffled Frog Leaping Algorithm

CZ:

Candidates Zone

EGNs:

Energy Gauge Nodes

RTT:

Round Trip Time

SPR:

Strength of Packet Reply

RREQ:

Route REQuest

MS:

Mobile Sink

UCB:

Upper Confidence Bound

RREP:

Route REPly

UM-MAB:

Multi-User Multi-Armed Bandit

PLR:

Packet Loss Ratio

VH:

Virtual Head

EBAR:

Energy-Efficient Load Balancing Ant-based Routing

GWO:

Grey Wolf Optimization

MPAR:

Multi-sink Placement and Anycast Routing

GA:

Genetic Algorithm

EMPAR:

Extended Multi-sink Placement and Anycast Routing

PSO:

Particle Swarm Optimization

AODV:

Ad hoc On-Demand Distance Vector

ACO:

Ant Colony Optimization

CBERP:

Cluster Based Energy Efficient Protocol

BS:

Base Station

CS:

Compressive Sensing

ROI:

Region of Interest

PDR:

Packet Delivery Ratio

PEAR:

Predictive Energy-Aware Routing

SW-WSN:

Small-World Wireless Sensor Network

DCDG-ARW:

Dynamic Compressive Data Gathering using Angle-based Random Walk

MULE:

Mobile Ubiquitous Local Area Network Extensions

References

  1. Kamal A and Hamid Md Abdul 2017 Supervisory routing control for dynamic load balancing in low data rate wireless sensor networks. Wireless Networks. 23: 1085–1099

    Article  Google Scholar 

  2. Low C P, Fang C, Ng J M and Ang Y H 2008 Efficient load balanced clustering algorithms for wireless sensor networks. Computer Communications. 31(4): 750–759

    Article  Google Scholar 

  3. Kuila P, Gupta S K and Jana P K 2013 A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation. 12: 48–56

    Article  Google Scholar 

  4. So J and Byun H 2017 Load-Balanced Opportunistic Routing for Duty-Cycled Wireless Sensor Networks. IEEE Transactions on Mobile Computing. 16(7): 1940–1955

    Article  Google Scholar 

  5. Kacimi R, Dhaou R and Beylot A L 2013 Load balancing techniques for lifetime maximizing in wireless sensor networks. Ad Hoc Networks Journal. 11(8): 2172–2186

    Article  Google Scholar 

  6. Selvakumar K and Pattabirani G 2019 A clustered fuzzy and dynamically well organized load balancing algorithm (CFDLB) for network life time enhancement in wireless sensor networks. International Journal of Innovative Technology and Exploring Engineering. 8(4): 472–479

    Google Scholar 

  7. Palani U, Amuthavalli G and Alamelumangai V 2020 Secure and load-balanced routing protocol in wireless sensor network or disaster management. IET Information Security. 14(5): 513–520

    Article  Google Scholar 

  8. Wang Tianshu, Yang Xichen, Kongfa Hu and Zhang Gongxuan 2021 A Distributed Load Balancing Clustering Algorithm for Wireless Sensor Networks. Wireless Personal Communications. 120(4): 3343–3367

    Article  Google Scholar 

  9. Smys S and Haoxiang Wang 2021 A secure optimization algorihtm for Quality-Of-Service improvement in Hybrid wireless networks. IRO Journal on Sustainable Wireless Systems. 3(1): 1–10

    Article  Google Scholar 

  10. Chanak P, Banerjee I and Rahaman H 2015 Load management scheme for energy holes reduction in wireless sensor networks. Computers and Electrical Engineering. 48: 343–357

    Article  Google Scholar 

  11. Wang E, Li H and Zhang S 2019 Load balancing based on cache resource allocation in satellite networks. IEEE Access. 7: 56864–56879

    Article  Google Scholar 

  12. Zhao M, Yang Y and Wang C 2015 Mobile data gathering with load bal anced clustering and dual data uploading in wireless sensor networks. IEEE Transactions on Mobile Computing. 14(4): 770–785

    Article  Google Scholar 

  13. Kuila P and Jana P K 2012 Improved load balanced clustering algorithm for wireless sensor networks. In: International Conference on Advanced Computing, Networking and Security, ADCONS 2011, Springer, Berlin, Heidelberg, 2012, pp. 399 – 404, 2012

  14. Edla D R, Kongara M C and Cheruku R 2019 SCE-PSO Based clustering approach for load balancing of gateways in wireless sensor networks. Wireless Networks. 25: 1067–1081

    Article  Google Scholar 

  15. Edla D R, Lipare A and Cheruku R 2018 Shuffled complex evolution approach for load balancing of gateways in wireless sensor networks. Wireless Personal Communications. 98(4): 3455–3476

    Article  Google Scholar 

  16. Edla D R, Lipare A, Cheruku R and Kuppili V 2017 An Efficient Load Balancing of Gateways using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs. IEEE Sensors Journal. 17(20): 6724–6733

    Article  Google Scholar 

  17. Hawbani A, Wang X, Sharabi Y, Ghannami A, Kuhlani H and Karmoshi S 2021 LORA: Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSN. IEEE Transactions on Mobile Computing. 18(7): 1601–1615

    Article  Google Scholar 

  18. Chatterjee P, Ghosh S C and Das N 2017 Load Balanced Coverage with Graded Node Deployment in Wireless Sensor Networks. IEEE Transactions on Multi-Scale Computing Systems. 3(2): 100–112

    Article  Google Scholar 

  19. Liu X and Zhang P 2018 Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks. IEEE Communications Letters. 22(1): 125–128

    Article  MathSciNet  Google Scholar 

  20. Adil M, Khan R, Almaiah M A, Binsawad M, Ali J, Al-Saaidah A and Ta Q T H 2020 An Efficient Load Balancing Scheme of Energy Gauge Nodes to Maximize the Lifespan of Constraint Oriented Networks. IEEE Access. 8: 148510–148527

    Article  Google Scholar 

  21. Zhang J, Tang J and Wang F 2020 Cooperative Relay Selection for Load Balancing With Mobility in Hierarchical WSNs: A Multi-Armed Bandit Approach. IEEE Access. 8: 18110–18122

    Article  Google Scholar 

  22. Li X, Keegan B, Mtenzi F, Weise T and Tan M 2019 Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks. IEEE Access. 7: 113182–113196

    Article  Google Scholar 

  23. Yunjian Tang, Weiren Shi, Jun Yi and Yanxia Wang 2011 Dynamic Load-balancing Algorithm of WSN for Data Gathering Application. Computer Engineering and Applications. 47(6): 122–126

    Google Scholar 

  24. Mohajerani A and Gharavian D 2016 An Ant colony optimization based routing algorihtm for extending network lifetime in wireless sensor networks. Wireless Networks. 22(8): 2637–2647

    Article  Google Scholar 

  25. Yao Y, Cao Q and Vasilakos A V 2015 EDAL: An energy efficienty, delay-aware, and lifetime-balancing data collection protcol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking. 23(3): 810–823

    Article  Google Scholar 

  26. Gowri S, Anandhamala G S and Divya G 2014 Enhancing the Digital Data Retrieval System Using Novel Techniques. Journal of Theoretical and Applied Information Technology. 66(2): 481–489

    Google Scholar 

  27. Janani S, Ramaswamy M and Samuel Manoharan J 2018 Clustered HEED scheme for congestion avoidance in cognitive radio sensor network. Journal of Theoretical and Applied Information Technology. 96(17): 5674–5684

    Google Scholar 

  28. Sharma R, Vashisht V and Singh U 2019 EEFCM-DE: energy-efficient clus tering based on fuzzy C means and differential evolution algorithm in WSNs. IET Communications. 13(8): 996–1007

    Article  Google Scholar 

  29. Pandey O J and Hegde R M 2018 Low-Latency and Energy-Balanced Data Transmission Over Cognitive Small World WSN. IEEE Transactions on Vehicular Technology. 67(8): 7719–7733

    Article  Google Scholar 

  30. Jecan Eusebiu Eusebiu, Pop Catalin, Ratiu Ovidiu and Puschita Emanuel 2022 Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform. Sensors. 22(6): 2107. https://doi.org/10.3390/s22062107

    Article  Google Scholar 

  31. Pandey O J, Mahajan A and Hegde R M 2018 Joint Localization and Data Gathering Over a Small-World WSN With Optimal Data MULE Allocation. IEEE Transactions on Vehicular Technology. 67(7): 6518–6532

    Article  Google Scholar 

  32. Shima Pakdaman Tirani, Avid Avokh and Jamshid Abouei 2022 Dynamic Compressive Data Gathering using Angle-based Random Walk in Hybrid WSNs. Ad Hoc Networks. 127 https://doi.org/10.1016/j.adhoc.2021.102770

Download references

Funding

The author did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of ECE, Sir Isaac Newton College of Engineering & Technology, Nagapattinam, Tamil Nadu, 611102, India

    J SAMUEL MANOHARAN

Authors
  1. J SAMUEL MANOHARAN
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The author is solely responsible for the experimental works conducted in this paper, drafting of the paper and presentation of all the sections.

Corresponding author

Correspondence to J SAMUEL MANOHARAN.

Ethics declarations

Conflicts of interest:

The author has no relevant financial or non-financial interests to disclose.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

MANOHARAN, J.S. Double attribute based node deployment in wireless sensor networks using novel weight based clustering approach. Sādhanā 47, 166 (2022). https://doi.org/10.1007/s12046-022-01939-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-022-01939-7

Keywords

Search

Navigation