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Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks

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Abstract

The Internet of things (IoT) is an omnipresent system that can be accessed from a long distance, linking a variety of devices (things), including wireless sensor networks (WSNs). Cyber-physical systems monitor things from a distance and control them. Because of its widespread usage in a variety of applications, WSN is among the most essential contributors to the IoT and plays a key part in the daily lives of people. The battery’s energy is a vital source in the sensor node, impacting the lifespan of the WSN. Energy scarcity is a serious concern in WSN, as a large volume of redundant data is gathered and transferred on a regular basis. As a result, efficient energy consumption is the fundamental approach to maximizing network lifetime. This article proposes a two-level data reduction approach for use at two network levels: sensor nodes and gateways (GWs). A novel Compression-Based Data Reduction (CBDR) technology and an effective transmitting data strategy derived from data correlation are being developed at the sensor node level. These strategies are designed to more efficiently compress data readings from IoT devices. CBDR compresses data in two stages: lossy SAX quantization and lossless LZW compression. The suggested approaches function as filtering at the GW level, allowing the GW to discover and subsequently delete groups of data that are duplicated and provided by surrounding nodes. At this level, two strategies are advised: the first is based on the data compression concept, and the second is to identify all couples of member nodes that produce duplicated sets so that redundancy may be eliminated before they are delivered to the sink. The proposed solutions are evaluated using extensive simulation tests made available by the network’s OMNeT++ simulator. The proposed methodologies’ efficiency is tested using four related works: the PFF protocol, the ATP protocol, the AVMDA protocol, and the PIP-DA protocol. The proposed solution uses up to 79%, 80%, 90%, and 6% less for each of the remaining data, transmitted data, energy, and data loss, respectively, depending on the results.

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Data availability

The data that support the findings of this study are openly available in [Intel Lab Data] at [35].

Code availability

The software application or custom code used to solve the proposed methods of this study is available from the corresponding author upon request.

References

  1. Ratasich D, Khalid F, Geissler F, Grosu R, Shafique M, Bartocci E (2019) A roadmap toward the resilient internet of things for cyber-physical systems. IEEE Access 7:13260–13283. https://doi.org/10.1109/ACCESS.2019.2891969

    Article  Google Scholar 

  2. Al-Qurabat AKM, Idrees AK, Abou Jaoude C (2020) Dictionary-Based DPCM Method for Compressing IoT Big Data. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp 1290-1295. IEEE, 2020. https://doi.org/10.1109/IWCMC48107.2020.9148492

  3. Xu G, Shi Y, Sun X, Shen W (2019) Internet of things in marine environment monitoring: a review. Sensors 19(7):1711. https://doi.org/10.3390/s19071711

    Article  Google Scholar 

  4. Liu X, Sheng Z, Yin C (2017) Routing protocol for low power and lossy IoT networks. In: From internet of things to smart cities, Chapman and Hall/CRC, pp 89-118

  5. Al-Qurabat AKM, Idrees AK (2020) Data gathering and aggregation with selective transmission technique to optimize the lifetime of internet of things networks. Int J Commun Syst 33(11):e4408. https://doi.org/10.1002/dac.4408

    Article  Google Scholar 

  6. Al-Qurabat AKM, Abdulzahra SA (2020) An overview of periodic wireless sensor networks to the internet of things. In: IOP Conference Series: Materials Science and Engineering, 928(3): 032055. IOP Publishing. https://doi.org/10.1088/1757-899X/928/3/032055

  7. Abdulzahra SA, Al-Qurabat AKM, Idrees AK (2020) Data reduction based on compression technique for big data in IoT. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), IEEE., pp 103–108.https://doi.org/10.1109/ESCI48226.2020.9167636

  8. Bahi JM, Makhoul A, Medlej M (2014) A two tiers data aggregation scheme for periodic sensor networks. Adhoc & Sens Wirel Netw, 21(1)

  9. Harb H, Makhoul A, Couturier R, Medlej M (2015) ATP: an aggregation and transmission protocol for conserving energy in periodic sensor networks. In: 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, IEEE, pp 134–139) https://doi.org/10.1109/WETICE.2015.9

  10. Harb H, Makhoul A, Laiymani D, Bazzi O, Jaber A (2015) An analysis of variance-based methods for data aggregation in periodic sensor networks. In: Transactions on large-scale data-and knowledge-centered systems XXII, Springer, Berlin, pp 165-183. https://doi.org/10.1007/978-3-662-48567-5_6

  11. Saeedi IDI, Al-Qurabat AKM (2022) Perceptually important points-based data aggregation method for wireless sensor networks. Baghdad Sci J 19(4):0875. https://doi.org/10.21123/bsj.2022.19.4.0875

    Article  Google Scholar 

  12. Marascu A, Pompey P, Bouillet E, Wurst M, Verscheure O, Grund M, Cudre-Mauroux P (2014) TRISTAN: real-time analytics on massive time series using sparse dictionary compression. In: 2014 IEEE International Conference on Big Data (Big Data), IEEE, pp 291–300. https://doi.org/10.1109/BigData.2014.7004244

  13. Khelifati A, Khayati M, Cudré-Mauroux P (2019) CORAD: correlation-aware compression of massive time series using sparse dictionary coding. In: 2019 IEEE International Conference on Big Data (Big Data), IEEE, pp 2289–2298. https://doi.org/10.1109/BigData47090.2019.9005580

  14. Pope J, Vafeas A, Elsts A, Oikonomou G, Piechocki R, Craddock I (2018) An accelerometer lossless compression algorithm and energy analysis for IoT devices. In: 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), IEEE, pp 396–401. https://doi.org/10.1109/WCNCW.2018.8368985

  15. Le TL, Vo MH (2018) Lossless data compression algorithm to save energy in wireless sensor network. In: 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), IEEE, pp 597–600. https://doi.org/10.1109/GTSD.2018.8595614

  16. Eichinger F, Efros P, Karnouskos S, Böhm K (2015) A time-series compression technique and its application to the smart grid. VLDB J 24(2):193–218. https://doi.org/10.1007/s00778-014-0368-8

    Article  Google Scholar 

  17. Hawkins SEI, Darlington EH (2012) Algorithm for compressing time-series data. NASA Tech Briefs

  18. Al-Qurabat AKM, Mohammed ZA, Hussein ZJ (2021) Data traffic management based on compression and MDL techniques for smart agriculture in IoT. Wirel Pers Commun 120(3):2227–2258. https://doi.org/10.1007/s11277-021-08563-4

    Article  Google Scholar 

  19. Toulni Y, Belhoussine Drissi T, Nsiri B (2021) ECG signal diagnosis using discrete wavelet transform and K-nearest neighbor classifier. In: Proceedings of the 4th International Conference on Networking, Information Systems & Security, pp 1–6. https://doi.org/10.1145/3454127.3457628

  20. Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D Nonlinear Phenomena 404:132306. https://doi.org/10.1016/j.physd.2019.132306

    Article  MathSciNet  MATH  Google Scholar 

  21. Saad G, Harb H, Abou Jaoude C, Jaber A (2019) A distributed round-based prediction model for hierarchical large-scale sensor networks. In: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp 1–6, IEEE. https://doi.org/10.1109/WiMOB.2019.8923312

  22. Mogahed HS, Yakunin AG (2018) Development of a lossless data compression algorithm for multichannel environmental monitoring systems. In: 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE), IEEE, pp 483–486. https://doi.org/10.1109/APEIE.2018.8546121

  23. Blalock D, Madden S, Guttag J (2018) Sprintz: time series compression for the internet of things. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(3):1–23. https://doi.org/10.1145/3264903

    Article  Google Scholar 

  24. Spiegel J, Wira P, Hermann G (2018) A comparative experimental study of lossless compression algorithms for enhancing energy efficiency in smart meters. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), IEEE, pp 447–452. https://doi.org/10.1109/INDIN.2018.8471921

  25. Campobello G, Segreto A, Zanafi S, Serrano S (2017) RAKE: a simple and efficient lossless compression algorithm for the internet of things. In: 2017 25th European Signal Processing Conference (EUSIPCO), IEEE, pp 2581–2585. https://doi.org/10.23919/EUSIPCO.2017.8081677

  26. Al-Qurabat AKM, Salman HM, Finjan AAR (2022) Important extrema points extraction-based data aggregation approach for elongating the WSN lifetime. Int J Comput Appl Technol, 67(4), In press

  27. Jawad GAM, Al-Qurabat AKM, Idrees AK (2022) Maximizing the underwater wireless sensor networks’ lifespan using BTC and MNP5 compression techniques. Ann Telecommun, pp 1–21, In press. https://doi.org/10.1007/s12243-021-00903-6

  28. Fomina M, Antipov S, Vagin V (2016) Methods and algorithms of anomaly searching in collections of time series. In: Proceedings of the First International Scientific Conference Intelligent Information Technologies for Industry (IITI’16), Springer, Cham, pp 63–73. https://doi.org/10.1007/978-3-319-33609-1_6

  29. Eichinger F, Efros P, Karnouskos S, Böhm K (2015) A time-series compression technique and its application to the smart grid. VLDB J 24(2):193–218. https://doi.org/10.1007/s00778-014-0368-8

    Article  Google Scholar 

  30. Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM, pp 2–11. https://doi.org/10.1145/882082.882086

  31. Sayood K (2017) Introduction to data compression. Morgan Kaufmann, Burlington

    MATH  Google Scholar 

  32. Liu C, Luo J, Song Y (2015) Correlation-model-based data aggregation in wireless sensor networks. In: 2015 12th international Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, pp 822–827. https://doi.org/10.1109/FSKD.2015.7382049

  33. Hartigan JA (1975) Clustering algorithms. John Wiley & Sons. Inc., New York

    MATH  Google Scholar 

  34. Varga A (2010) OMNeT++. Modeling and tools for network simulation. Springer, Heidelberg, pp 35–59

    Chapter  Google Scholar 

  35. Peter B, Wei H, Carlos G, Sam M, Mark P, Romain T (2004) Intel berkeley research lab. http://db.csail.mit.edu/labdata/labdata.html. [Online; accessed 2-July-2021]

  36. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, IEEE, p 10. https://doi.org/10.1109/hicss.2000.926982

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Acknowledgements

The authors would like to gratefully acknowledge the University of Babylon, Iraq, for the supported.

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Authors and Affiliations

  1. Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq

    Ali Kadhum M. Al-Qurabat & Ali Kadhum Idrees

  2. Department of Dentistry, Al-Mustaqbal University College, Babylon, Iraq

    Suha Abdulhussein Abdulzahra

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  1. Ali Kadhum M. Al-Qurabat
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  2. Suha Abdulhussein Abdulzahra
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  3. Ali Kadhum Idrees
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Correspondence to Ali Kadhum M. Al-Qurabat.

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Al-Qurabat, A.K.M., Abdulzahra, S.A. & Idrees, A.K. Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks. J Supercomput 78, 17844–17890 (2022). https://doi.org/10.1007/s11227-022-04548-7

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