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BFL: a buffer based linear filtration method for data aggregation in wireless sensor networks

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Abstract

The rapid growth of the internet and advancement in both hardware and software leads to many new inventions; Wireless Sensor Network (WSN) is among them. WSN utilizes the flexibility provided by wireless communication, along with the strong sensing capabilities of sensor nodes. WSN are smart devices capable of monitoring their nearby environment and sending the data to the base station (BS) for information building. These advanced devices come with a limitation of energy. Several research papers have been published that encounter this limitation and suggest techniques to overcome it. There exist different routing mechanisms to reduce transmission costs and provide efficient solutions. However, another factor of improvement is data communication. This work proposes a Buffer-based linear filtering (BFL) method for Data Aggregation while Reducing Correlations in data that reduces data transmission to improve performance. The proposed approach aims at reducing data load by eliminating space and time correlations. The result shows that the proposed model works better than the others in terms of transmission cost reduction, energy utilization, and information accuracy.

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References

  1. Akyildiz IF, Vuran MC (2010) Wireless sensor networks, vol 4. John Wiley & Sons, New York

    Book  Google Scholar 

  2. Rastogi A, Rai S (2021) A novel protocol for stable period and lifetime enhancement in WSN. Int J Inf Technol 13(2):777–783

    Google Scholar 

  3. Gupta S, Sahoo AK, Sahoo UK (2021) Volterra and Wiener model based temporally and spatio-temporally coupled nonlinear system identification: a synthesized review. IETE Tech Rev 38(3):303–327

    Article  Google Scholar 

  4. 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 

  5. 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 

  6. Chauhan N, Chauhan S (2021) Partly centralized partly distributed energy efficient sleep/wake scheduling in wireless sensor networks for applications requiring continuous sensing. IETE Tech Rev. https://doi.org/10.1080/02564602.2021.1934906

    Article  Google Scholar 

  7. Sarangi K, Bhattacharya I (2019) A study on data aggregation techniques in wireless sensor network in static and dynamic scenarios. Innov Syst Softw Eng 15(1):3–16

    Article  Google Scholar 

  8. Nagesh R, Raga S, Mishra S (2019) Elimination of redundant data to enhance wireless sensor network performance using multi level data aggregation technique. In: 2019 10th international conference on computing, communication and networking technologies (ICCCNT). IEEE, p 1–5

  9. Dehkordi SA, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Dehkordi MA (2020) A survey on data aggregation techniques in IoT sensor networks. Wireless Netw 26(2):1243–1263

    Article  Google Scholar 

  10. 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 

  11. 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

    Article  Google Scholar 

  12. Agarwal A, Dev A (2020) A multi-layer data-driven clustering based protocol for sensor networks. Int J Sci Technol Res 9(3):5092–5096

    Google Scholar 

  13. Cheng S, Cai Z, Li J (2017) Approximate sensory data collection: a survey. Sensors 17(3):564

    Article  Google Scholar 

  14. Wan R, Xiong N, Hu Q, Wang H, Shang J (2019) Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks. EURASIP J Wirel Commun Netw 2019(1):1–11

    Article  Google Scholar 

  15. Fang W, Wen X, Xu J, Zhu J (2019) CSDA: a novel cluster-based secure data aggregation scheme for WSNs. Clust Comput 22(3):5233–5244

    Article  Google Scholar 

  16. 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 

  17. Naureen A, Zhang N (2019) Competition: alternating multicast with aggregated data collection in wireless sensor networks. In: EWSN, p 296–297

  18. Mosavvar I, Ghaffari A (2019) Data aggregation in wireless sensor networks using firefly algorithm. Wireless Pers Commun 104(1):307–324

    Article  Google Scholar 

  19. Agarwal A, Dev A (2020) Extended RSSI based cluster head selection algorithm for wireless sensor networks. Int J Futur Gener Commun Netw 13(1):559–568

    Google Scholar 

  20. Tan R, Xing G, Liu X, Yao J, Yuan Z (2010) Adaptive calibration for fusion-based wireless sensor networks. In: 2010 Proceedings IEEE INFOCOM. IEEE, p 1–9

  21. Tang X, Xie H, Chen W, Niu J, Wang S (2017) Data aggregation based on overlapping rate of sensing area in wireless sensor networks. Sensors 17(7):1527

    Article  Google Scholar 

  22. Bongale AM, Nirmala CR, Bongale AM (2020) Energy efficient intra-cluster data aggregation technique for wireless sensor network. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00419-7

    Article  Google Scholar 

  23. Kumar R, Gangwar M (2019) Improved BEST-MAC protocol for WSN using optimal cluster head selection. Int J Inf Technol. https://doi.org/10.1007/s41870-019-00385-9

    Article  Google Scholar 

  24. Jain K, Kumar A (2021) A lightweight data transmission reduction method based on a dual prediction technique for sensor networks. Trans Emerg Telecommun Technol 32:e4345

    Google Scholar 

  25. Jain K, Kumar A, Jha CK (2019) Probabilistic-based energy-efficient single-hop clustering technique for sensor networks. Communication and intelligent systems. ICCIS. Springer, Singapore

    Google Scholar 

  26. De Vito S, Massera E, Piga M, Martinotto L, Di Francia G (2008) On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators, B Chem 129(2):750–757

    Article  Google Scholar 

  27. Mukhopadhyay S, Schurgers C, Panigrahi D, Dey S (2008) Model-based techniques for data reliability in wireless sensor networks. IEEE Trans Mob Comput 8(4):528–543

    Article  Google Scholar 

  28. Haas C, Wilke J (2011) Energy evaluations in wireless sensor networks: a reality check. In: Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, p 27–30

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Acknowledgements

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of CSE, Ramanujan College, New Delhi, India

    Arun Agarwal

  2. Department of CSE, School of Computing at DIT University, Dehradun, India

    Khushboo Jain

  3. IGDTUW, Delhi, India

    Amita Dev

Authors
  1. Arun Agarwal
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  2. Khushboo Jain
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  3. Amita Dev
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Corresponding author

Correspondence to Khushboo Jain.

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Agarwal, A., Jain, K. & Dev, A. BFL: a buffer based linear filtration method for data aggregation in wireless sensor networks. Int. j. inf. tecnol. 14, 1445–1454 (2022). https://doi.org/10.1007/s41870-022-00879-z

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  • DOI: https://doi.org/10.1007/s41870-022-00879-z

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