Skip to main content

Advertisement

SpringerLink
Log in

SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks

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

Abstract

To improve the network lifetime of battery-operated wireless sensor networks (WSNs), effective deployment of available low-power resources is required. This is essential as it is impractical to exchange or recharge the energy resource after deployment. To properly utilize the limited energy, a suitable cluster-based data aggregation technique is required, which considerably reduces the network’s overall energy usage. Therefore, this work proposes a cluster-based data aggregation technique called Scalable Clustering Algorithm for Data Aggregation (SCADA). Each cluster has a single cluster head (CH) and several cluster relays (CRs) in this algorithm. The number of CRs in a cluster varies depending on the cluster’s geographic position in relation to the Base Station. We assure that energy usage is reduced as a result of the multi-hop short-distance data transmission by doing so. In hybrid modes, the CHs are also rotated at the appropriate intervals to reduce control packets. The proposed algorithm is novel and well suited for both homogeneous and heterogeneous WSNs because it uses a hybrid CH selection mechanism, a threshold-based CH rotation mechanism, and a mechanism of allocating dedicated CR in each cluster. Effects of variations in the length of the sensing field, variation in SN density, variation in Cluster count, and variation in initial energy and heterogeneity test are used to assess the proposed algorithm’s efficiency. SCADA is incredibly scalable for large-scale WSN and energy-efficient with a small number of control messages as depicted by the analytical results. Simulation-based results, clearly states that as compared to three protocols EESCT, DUCF, and MUCA, SCADA obtains approximately 35%, 25%, and 10%, respectively, more rounds of communication. SCADA drastically improves when the length of the sensing field increases as compared to EESCT, DUCF, and MUCA. SCADA when compared to the EESCT, DUCF, and MUCA algorithms, the average energy consumption is significantly lower by 21 to 38%.

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

Similar content being viewed by others

Abbreviations

ACR:

Average communication range

BS:

Base station

CH:

Cluster head

CHNSN:

Cluster head to normal sensor node ratio

CMNs:

Cluster member nodes

CR:

Cluster relays

CRC:

Cycle error detection code

DUCF:

Distributed unequal clustering with fuzzy logic

EESCA-WR:

Energy-efficient clustering algorithm with relay

EESCT:

Energy-efficient cluster-based scheduling technique

Fs:

Free space

FT-CHSDA:

Fault tolerant CH selection and data aggregation scheme

GPS:

Global positioning system

HWSN:

Hierarchical wireless sensor network

MEACBM:

Mobile energy aware cluster based multi-hop

Mp:

Multi path

MUCA:

Multiple layers uneven clustering Algorithm

ORCHS:

Optimal RSSI CH selection

RSCT:

Resilient steady clustering technique

RSSI:

Radio signal strength index

SBN:

Standby node

SCADA:

Scalable cluster-based data aggregation

SNs:

Sensor nodes

TDMA:

Time-division multiple access

UID:

Unique identifier

WSNs:

Wireless sensor networks

References

  1. Pan J, Cai L, Hou YT, Shi Y, Shen SX (2005) Optimal base-station locations in two-tiered wireless sensor networks. IEEE Trans Mob Comput 4(5):458–473

    Article  Google Scholar 

  2. Jain K, Kumar A (2020) Energy-efficient data-aggregation technique for correlated spatial and temporal data in cluster-based sensor networks. Int J Bus Data Commun Netw (IJBDCN) 16(2):53–68

    Article  Google Scholar 

  3. Singh A, Jain K (2022) An automated lightweight key establishment method for secure communication in WSN. Wirel Pers Commun. https://doi.org/10.1007/s11277-022-09492-6

    Article  Google Scholar 

  4. Agarwal A, Dev A, Jain K (2020) Prolonging sensor network lifetime by using energy-efficient cluster-based scheduling. Int J Sci Technol Res, 9(04)

  5. Dhand G, Tyagi SS (2016) Data aggregation techniques in WSN: survey. Proc Comput Sci 92:378–384

    Article  Google Scholar 

  6. Rault T, Bouabdallah A, Challal Y (2014) Energy efficiency in wireless sensor networks: a top-down survey. Comput Netw 67:104–122

    Article  Google Scholar 

  7. Tokognon CA, Gao B, Tian GY, Yan Y (2017) Structural health monitoring framework based on Internet of Things: a survey. IEEE Internet Things J 4(3):619–635

    Article  Google Scholar 

  8. Salam A (2020) Internet of things for water sustainability. In: Salam A (ed) Internet of Things for sustainable community development. Springer, Cham, pp 113–145

    Chapter  Google Scholar 

  9. Aziz ZAA, Ameen SYA (2021) Air pollution monitoring using wireless sensor networks. J Inf Technol Inform 1(1):20–25

    Google Scholar 

  10. Goyal N, Dave M, Verma AK (2019) Data aggregation in underwater wireless sensor network: recent approaches and issues. J King Saud Univ Comput Inform Sci 31(3):275–286

    Google Scholar 

  11. Jain K, Kumar A (2020) An energy-efficient prediction model for data aggregation in sensor network. J Ambient Intell Humaniz Comput 11(11):5205–5216

    Article  Google Scholar 

  12. Gupta K, Sikka V (2015) Design issues and challenges in wireless sensor networks. Int J Comput Appl 112(4):0975–8887

    Google Scholar 

  13. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): A vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  14. Jain K, Agarwal A, Kumar A (2021) A novel data prediction technique based on correlation for data reduction in sensor networks. In: Proceedings of international conference on artificial intelligence and applications (pp 595–606). Springer, Singapore

  15. Agarwal A, Gupta K, Yadav KP (2016) A novel energy efficiency protocol for WSN based on optimal chain routing. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp 368–373). IEEE

  16. Comito C, Talia D, Trunfio P (2011) An energy-aware clustering scheme for mobile applications. In: 2011 IEEE 11th International Conference on Computer and Information Technology (pp 15–22). IEEE

  17. Dziengel N, Seiffert M, Ziegert M, Adler S, Pfeiffer S, Schiller J (2016) Deployment and evaluation of a fully applicable distributed event detection system in Wireless Sensor Networks. Ad Hoc Netw 37:160–182

    Article  Google Scholar 

  18. Rostami AS, Badkoobe M, Mohanna F, Hosseinabadi AAR, Sangaiah AK (2018) Survey on clustering in heterogeneous and homogeneous wireless sensor networks. J Supercomput 74(1):277–323

    Article  Google Scholar 

  19. Jain K, Bhola A (2018) Data aggregation design goals for monitoring data in wireless sensor networks. J Netw Secur Comput Netw 4(3):1–9

    Google Scholar 

  20. Chan L, Chavez KG, Rudolph H, Hourani A (2020) Hierarchical routing protocols for wireless sensor network: a compressive survey. Wireless Netw 26(5):3291–3314

    Article  Google Scholar 

  21. Maratha P, Gupta K (2019) A comprehensive and systematized review of energy-efficient routing protocols in wireless sensor networks. Int J Comput Appl, 1–18

  22. Comito C, Talia D (2017) Energy consumption of data mining algorithms on mobile phones: evaluation and prediction. Pervasive Mob Comput 42:248–264

    Article  Google Scholar 

  23. Baranidharan B, Santhi B (2016) DUCF: distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl Soft Comput 40:495–506

    Article  Google Scholar 

  24. Liu J, Su S, Lu Y, Dong J (2018) Multiple layers uneven clustering algorithm based on residual energy for wireless sensor networks. J Eng 2018(16):1555–1560

    Article  Google Scholar 

  25. Toor AS, Jain AK (2019) Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks. AEU-Int J Electron Commun 102:41–53

    Article  Google Scholar 

  26. Jain K, Kumar A (2019) An optimal RSSI-based cluster-head selection for sensor networks. Int J Adapt Innov Syst 2(4):349–361

    Article  Google Scholar 

  27. Padmanaban Y, Muthukumarasamy M (2020) Scalable grid-based data gathering algorithm for environmental monitoring wireless sensor networks. IEEE Access 8:79357–79367

    Article  Google Scholar 

  28. Jain K, Kumar A, Jha CK (2019) Probabilistic-based energy-efficient single-hop clustering technique for sensor networks. Commun Intell Syst. ICCIS

  29. Jain K, Kumar A, Vyas V (2020) A resilient steady clustering technique for sensor networks. Int J Appl Evolut Comput (IJAEC) 11(4):1–12

    Article  Google Scholar 

  30. Chanak P, Banerjee I, Sherratt RS (2020) A green cluster-based routing scheme for large-scale wireless sensor networks. Int J Commun Syst 33(9):e4375

    Article  Google Scholar 

  31. Jain K, Singh A (2021) An empirical cluster head selection and data aggregation scheme for a fault-tolerant sensor network. Int J Distrib Syst Technol (IJDST) 12(3):27–47. https://doi.org/10.4018/IJDST.2021070102

    Article  Google Scholar 

  32. Hasheminejad E, Barati H (2021) A reliable tree-based data aggregation method in wireless sensor networks. Peer-to-Peer Netw Appl 14(2):873–887

    Article  Google Scholar 

  33. Goyal N, Dave M, Verma AK (2020) SAPDA: secure authentication with protected data aggregation scheme for improving QoS in scalable and survivable UWSNs. Wireless Pers Commun 113(1):1–15

    Article  Google Scholar 

  34. Agarwal A, Jain K, Dev A (2022) BFL: a buffer based linear filtration method for data aggregation in wireless sensor networks. Int J Inf Technol. https://doi.org/10.1007/s41870-022-00879-z

    Article  Google Scholar 

  35. Pakdel H, Fotohi R (2021) A firefly algorithm for power management in wireless sensor networks (WSNs). J Supercomput 77(9):9411–9432

    Article  Google Scholar 

  36. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless mi-crosensor networks. In: Proceedings of the 33rd annual Hawaii inter-national conference on system sciences (pp 10-pp). IEEE

  37. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670

    Article  Google Scholar 

  38. Jain K, Kumar A (in press) An innovative framework for balanced cluster-based data aggregation in Sensor Networks. Int J Commun Syst

  39. Issariyakul T, Hossain E (2009) Introduction to network simulator 2 (NS2). In: Issariyakul T, Hossain E (eds) Introduction to network simulator NS2. Springer, Boston, MA, pp 1–18

    Chapter  Google Scholar 

  40. Chernyshev M, Baig Z, Bello O, Zeadally S (2017) Internet of things (iot): Research, simulators, and testbeds. IEEE Internet Things J 5(3):1637–1647

    Article  Google Scholar 

  41. Kumar D, Aseri TC, Patel R (2009) EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32(4):662–667

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, DIT University, Dehradun, India

    Khushboo Jain

  2. Department of CSE, DTU, New Delhi, India

    Pawan Singh Mehra

  3. Department of CSE, Buddha Institute of Technology, Gida, Gorakhpur, India

    Anshu Kumar Dwivedi

  4. Department of CSE, Ramanujan College, University of Delhi, New Delhi, India

    Arun Agarwal

Authors
  1. Khushboo Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pawan Singh Mehra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anshu Kumar Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arun Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khushboo Jain.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals per-formed by any of the authors.

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

Jain, K., Mehra, P.S., Dwivedi, A.K. et al. SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks. J Supercomput 78, 13624–13652 (2022). https://doi.org/10.1007/s11227-022-04419-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04419-1

Keywords

Search

Navigation