Skip to main content
Springer Nature Link
Log in

New fog computing enabled lossless EEG data compression scheme in IoT networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The rapid evolution of the Internet of Things (IoT) leads to emerging several applications such as smart home, ehealth, smart agriculture and farming, etc. The edge of the IoT networks will receive a huge amount of data generated by these smart applications which regularly require to be delivered to the servers of the remote data centers for additional real-time treatment. However, transmitting the whole of these IoT data across the network toward the data center of the cloud will impose a high overhead on the IoT network. The data exchange and processing long delay have a significant impact on the response speed of the real-time IoT applications. It can reduce the time responsiveness of these applications. The fog computing has provided for the IoT applications as an intermediate layer between the smart end devices and the cloud to reduce IoT data and improve the response time for IoT applications. In this paper, new fog computing enabled lossless EEG data compression scheme is proposed to minimize the amount of IoT EEG data uploaded to the cloud. The EEG data compression scheme consists of two phases: clustering and compression. First, the received data is clustered into clusters using agglomerative hierarchical clustering. The Huffman encoding is applied to each resulted cluster in the second phase. Finally, the compressed files of smaller clusters are combined and uploaded from fog to cloud. Several experiments have been implemented and the comparison results show that the suggested combined Hierarchical Clustering and Huffman Encoding (HCHE) method significantly decreases the volume of EEG data uploaded to the cloud platform. The proposed HCHE method achieves 4.33 of average compression power that is more than twice as much the compression power of some existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdellatif AA, Emam A, Chiasserini CF, Mohamed A, Jaoua A, Ward R (2019) Edge-based compression and classification for smart healthcare systems: concept, implementation and evaluation. Expert Syst Appl 117:1–14

    Article  Google Scholar 

  • Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64(6):061907

    Article  Google Scholar 

  • Azar J, Darazi R, Habib C, Makhoul A, Demerjian J (2018) Using dwt lifting scheme for lossless data compression in wireless body sensor networks. In: 2018 14th International wireless communications & mobile computing conference (IWCMC), 25–29 June 2018. IEEE, Limassol, Cyprus, pp 1465–1470

  • Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, August 17, 2012, Helsinki, Finland, pp 13–16

  • Boylan GB, Kharoshankaya L, Wusthoff CJ (2015) Seizures and hypothermia: importance of electroencephalographic monitoring and considerations for treatment. Semin Fetal Neonatal Med 20:103–108

    Article  Google Scholar 

  • De Paola A, Ferraro P, Re GL, Morana M, Ortolani M (2019) A fog-based hybrid intelligent system for energy saving in smart buildings. J Ambient Intell Human Comput 11(7):2793–2807

    Article  Google Scholar 

  • Elaskary RM, Saeed M, Ismail T, Mostafa H, Gabran S (2017) Hybrid DCT/quantized Huffman compression for electroencephalography data. In: 2017 Japan-Africa conference on electronics, communications and computers (JAC-ECC), 18–20 Dec 2017. IEEE, Alexandria, Egypt, pp 111–114

    Chapter  Google Scholar 

  • Feng G, Jiang G, Li Z, Wang X (2016) Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation. Neurol Sci 37(6):843–849

    Article  Google Scholar 

  • Gia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing, 26–28 Oct 2015. IEEE, Liverpool, UK, pp 356–363

  • Han J, Zhao Y, Sun H, Chen J, Ke A, Xu G, Zhang H, Zhou J, Wang C (2018) A fast, open EEG classification framework based on feature compression and channel ranking. Front Neurosci 12:217

    Article  Google Scholar 

  • Harb H, Idrees AK, Jaber A, Makhoul A, Zahwe O, Taam MA (2017) Wireless sensor networks: a big data source in internet of things. Int J Sens Wirel Commun Control 7(2):93–109

    Google Scholar 

  • Hejrati B, Fathi A, Abdali-Mohammadi F (2017) Efficient lossless multi-channel EEG compression based on channel clustering. Biomed Signal Process Control 31:295–300

    Article  Google Scholar 

  • Hosseinpour F, Plosila J, Tenhunen H (2016) An approach for smart management of big data in the fog computing context. In: 2016 IEEE international conference on cloud computing technology and science (CloudCom), 12–15 Dec 2016. IEEE, Luxembourg, Luxembourg, pp 468–471

  • Idrees AK, Witwit AJH (2020) Energy-efficient load-balanced RPL routing protocol for internet of things (IoTs) networks. Int J Internet Technol Secur Trans. https://doi.org/10.1504/IJITST.2020.10030144

    Article  Google Scholar 

  • Idrees AK, Harb H, Jaber A, Zahwe O, Abou Taam M (2017) Adaptive distributed energy-saving data gathering technique for wireless sensor networks. In: 2017 IEEE 13th international conference on wireless and mobile computing, networking and communications (WiMob), 9–11 Oct 2017. IEEE, Rome, Italy, pp 55–62

  • Idrees AK, Al-Yaseen WL, Abou Taam M, Zahwe O (2018) Distributed data aggregation based modified k-means technique for energy conservation in periodic wireless sensor networks. In: 2018 IEEE Middle East and North Africa communications conference (MENACOMM), 18-20 April 2018. IEEE, Jounieh, Lebanon, pp 1–6

  • Idrees AK, Al-Qurabat AKM, Abou Jaoude C, Al-Yaseen WL (2019) Integrated divide and conquer with enhanced k-means technique for energy-saving data aggregation in wireless sensor networks. In: 2019 15th International wireless communications & mobile computing conference (IWCMC), 24-28 June 2019. IEEE, Tangier, Morocco, pp 973–978

  • Idrees AK, Alhussaini R, Salman MA (2020) Energy-efficient two-layer data transmission reduction protocol in periodic sensor networks of IoTs. Personal Ubiquitous Comput. https://doi.org/10.1007/s00779-020-01384-5

    Article  Google Scholar 

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

    Article  Google Scholar 

  • La QD, Ngo MV, Dinh TQ, Quek TQ, Shin H (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digit Commun Netw 5(1):3–9

    Article  Google Scholar 

  • Mehdipour F, Javadi B, Mahanti A (2016) Fog-engine: towards big data analytics in the fog. In: 2016 IEEE 14th international conference on dependable, autonomic and secure computing, 14th international conference on pervasive intelligence and computing, 2nd international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech), 8-12 Aug. 2016. IEEE, Auckland, New Zealand, pp 640–646

  • Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA (2017) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464

    Article  Google Scholar 

  • Prieto J, Amira A, Bajo J, Mazuelas S, De la Prieta F (2018) IoT approaches for distributed computing. Wirel Commun Mob Comput 2018:1–2

    Article  Google Scholar 

  • Rajasekar P, Pushpalatha M (2020) Huffman quantization approach for optimized eeg signal compression with transformation technique. Soft Comput 24:14545–14559

    Article  Google Scholar 

  • Reddy V, Gayathri P (2020) Energy efficient data transmission in WSN thru compressive slender penetrative etiquette. J Ambient Intell Human Comput 11:4681–4693

    Article  Google Scholar 

  • Robinson Raju MM, Moh TS (2019) Compression of wearable body sensor network data. In: Smart data: state-of-the-art perspectives in computing and applications, Smart Data, 1st edn. Chapman and Hall/CRC

  • Rocha Filho GP, Meneguette RI, Maia G, Pessin G, Gonçalves VP, Weigang L, Ueyama J, Villas LA (2020) A fog-enabled smart home solution for decision-making using smart objects. Future Gener Comput Syst 103:18–27

    Article  Google Scholar 

  • Sahu M, Sharma Y, Sharma D, Bajpai S (2018) Feature compression using PCA on motor imagery classifications. In: Proceedings of 3rd international conference on internet of things and connected technologies (ICIoTCT), March 26–27, 2018. Malaviya National Institute of Technology, Jaipur, India, pp 26–27

  • Sarasa G, Granados A, Rodriguez FB (2019) Algorithmic clustering based on string compression to extract p300 structure in EEG signals. Comput Methods Programs Biomed 176:225–235

    Article  Google Scholar 

  • Srinivasan K, Reddy MR (2010) Efficient preprocessing technique for real-time lossless EEG compression. Electron Lett 46(1):26–27

    Article  Google Scholar 

  • Srinivasan K, Dauwels J, Reddy MR (2011) A two-dimensional approach for lossless EEG compression. Biomed Signal Process Control 6(4):387–394

    Article  Google Scholar 

  • Swaroop KN, Chandu K, Gorrepotu R, Deb S (2019) A health monitoring system for vital signs using IoT. Internet Things 5:116–129

    Article  Google Scholar 

  • Xu G, Han J, Zou Y, Zeng X (2015) A 1.5-d multi-channel EEG compression algorithm based on NLSPIHT. IEEE Signal Process Lett 22(8):1118–1122

    Article  Google Scholar 

  • Yim O, Ramdeen KT (2015) Hierarchical cluster analysis: comparison of three linkage measures and application to psychological data. Quant Methods Psychol 11(1):8–21

    Article  Google Scholar 

  • Zhou S, Xu Z, Liu F (2016) Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans Neural Netw Learn Syst 28(12):3007–3017

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Babylon, Babylon, Iraq

    Sara Kadhum Idrees & Ali Kadhum Idrees

Authors
  1. Sara Kadhum Idrees
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ali Kadhum Idrees
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ali Kadhum Idrees.

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

Idrees, S.K., Idrees, A.K. New fog computing enabled lossless EEG data compression scheme in IoT networks. J Ambient Intell Human Comput 13, 3257–3270 (2022). https://doi.org/10.1007/s12652-021-03161-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03161-5

Keywords