Skip to main content
SpringerLink
Log in

Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications

  • Chapter
  • First Online:
Cloud Computing for Optimization: Foundations, Applications, and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 39))

Abstract

This book chapter discusses the concept of edge-assisted cloud computing and its relation to the emerging domain of “Fog-of-things (FoT)”. Such systems employ low-power embedded computers to provide local computation close to clients or cloud. The discussed architectures cover applications in medical, healthcare, wellness and fitness monitoring, geo-information processing, mineral resource management, etc. Cloud computing can get assistance by transferring some of the processing and decision making to the edge either close to client layer or cloud backend. Fog of Things refers to an amalgamation of multiple fog nodes that could communicate with each other with the Internet of Things. The clouds act as the final destination for heavy-weight processing, long-term storage and analysis. We propose application-specific architectures GeoFog and Fog2Fog that are flexible and user-orientated. The fog devices act as intermediate intelligent nodes in such systems where these could decide if further processing is required or not. The preliminary data analysis, signal filtering, data cleaning, feature extraction could be implemented on edge computer leading to a reduction of computational load in the cloud. In several practical cases, such as tele healthcare of patients with Parkinson’s disease, edge computing may decide not to proceed for data transmission to cloud (Barik et al., in 5th IEEE Global Conference on Signal and Information Processing 2017, IEEE, 2017) [4]. Towards the end of this research paper, we cover the idea of translating machine learning such as clustering, decoding deep neural network models etc. on fog devices that could lead to scalable inferences. Fog2Fog communication is discussed with respect to analytical models for power savings. The book chapter concludes by interesting case studies on real world situations and practical data. Future pointers to research directions, challenges and strategies to manage these are discussed as well. We summarize case studies employing proposed architectures in various application areas. The use of edge devices for processing offloads the cloud leading to an enhanced efficiency and performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Amiri, Application placement and backup service in computer clustering in software as a service (SaaS) networks. Comput. Oper. Res. 69, 48–55 (2016)

    Article  MathSciNet  Google Scholar 

  2. J. Andreu-Perez, C.C. Poon, R.D. Merrifield, S.T. Wong, G.Z. Yang, Big data for health. IEEE J. Biomed. Health Inf. 19(4), 1193–1208 (2015)

    Article  Google Scholar 

  3. R. Barik, H. Dubey, R.K. Lenka, K. Mankodiya, T. Pratik, S. Sharma, Mistgis: Optimizing geospatial data analysis using mist computing. in International Conference on Computing Analytics and Networking (ICCAN 2017) (Springer, 2017)

    Google Scholar 

  4. R. Barik, H. Dubey, K. Mankodiya, Soa-fog: Secure service-oriented edge computing architecture for smart health big data analytics. in 5th IEEE Global Conference on Signal and Information Processing 2017 (IEEE, 2017), p. 15

    Google Scholar 

  5. R.K. Barik, H. Dubey, A.B. Samaddar, R.D. Gupta, P.K. Ray, FogGIS: Fog computing for geospatial big data analytics. arXiv preprint http://arxiv.org/abs/1701.02601arXiv:1701.02601 (2016)

  6. R. Barik, H. Dubey, S. Sasane, R.K. Lenka, C. Misra, N. Simha, K. Mankodiya, Fog computing-based enhanced geohealth big data analysis. in 2017 International Conference on Intelligent Computing and Control, I2C2 (IEEE, 2017)

    Google Scholar 

  7. R. Barik, R.K. Lenka, H. Dubey, N.R. Simha, K. Mankodiya, Fog computing based SDI framework for mineral resources information infrastructure management in india. in 2017 International Conference on Intelligent Computing and Control, I2C2 (IEEE, 2017)

    Google Scholar 

  8. R. Barik, A. Samaddar, R. Gupta, Investigations into the efficacy of open source GIS software. Map World Forum (2009)

    Google Scholar 

  9. S. Bera, S. Misra, J.J. Rodrigues, Cloud computing applications for smart grid: A survey. IEEE Trans. Parallel Distribut. Syst. 26(5), 1477–1494 (2015)

    Article  Google Scholar 

  10. C.M. Bishop, Neural Networks for Pattern Recognition (Oxford university press, Oxford, 1995)

    Google Scholar 

  11. P. Boersma, D. Weenink, Praat-a System for Doing Phonetics by Computer [Computer Software] (Institute of Phonetic Sciences, University of Amsterdam, The Netherlands, 2003)

    Google Scholar 

  12. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things. in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing (ACM, 2012), p. 13–16

    Google Scholar 

  13. D. Borthakur, H. Dubey, N. Constant, L. Mahler, K. Mankodiya, Smart fog: Fog computing framework for unsupervised clustering analytics in wearable internet of things. in 5th IEEE Global Conference on Signal and Information Processing 2017 (IEEE, 2017), p. 15

    Google Scholar 

  14. A. Botta, W. De Donato, V. Persico, A. Pescape, Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)

    Article  Google Scholar 

  15. H.T. Chang, T.H. Lin, A database as a service for the healthcare system to store physiological signal data. PloS one 11(12), e0168935 (2016)

    Article  Google Scholar 

  16. F. Chen, H. Ren, Comparison of vector data compression algorithms in mobile gis. in 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 1, (IEEE, 2010), p. 613–617

    Google Scholar 

  17. Z. Chen, N. Chen, C. Yang, L. Di, Cloud computing enabled web processing service for earth observation data processing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(6), 1637–1649 (2012)

    Article  Google Scholar 

  18. M. Chiang, T. Zhang, Fog and iot: An overview of research opportunities. IEEE Internet Things J. 3(6), 854–864 (2016)

    Article  Google Scholar 

  19. N. Constant, D. Borthakur, M. Abtahi, H. Dubey, K. Mankodiya, Fog-assisted wIoT: A smart fog gateway for end-to-end analytics in wearable internet of things. arXiv preprint arXiv:1701.08680 (2017)

  20. A.V. Dastjerdi, H. Gupta, R.N. Calheiros, S.K. Ghosh, R. Buyya, Fog computing: Principles, architectures, and applications. arXiv preprint arXiv:1601.02752 (2016)

  21. S. Dey, A. Mukherjee, Robotic slam: a review from fog computing and mobile edge computing perspective. in Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services (ACM, 2016), p. 153–158

    Google Scholar 

  22. H. Dubey, N. Constant, K. Mankodiya, RESPIRE: A spectral kurtosis-based method to extract respiration rate from wearable ppg signals. in 2nd IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (IEEE, Philadelphia, USA, 2017)

    Google Scholar 

  23. H. Dubey, N. Constant, A. Monteiro, M. Abtahi, D. Borthakur, L. Mahler, Y. Sun, Q. Yang, K. Mankodiya, Fog computing in medical internet-of-things: Architecture, implementation, and applications. in Handbook of Large-Scale Distributed Computing in Smart Healthcare (Springer International Publishing AG, 2017)

    Chapter  Google Scholar 

  24. H. Dubey, J.C. Goldberg, K. Mankodiya, L. Mahler, A multi-smartwatch system for assessing speech characteristics of people with dysarthria in group settings. in 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom) (IEEE, 2015)

    Google Scholar 

  25. H. Dubey, R. Kumaresan, K. Mankodiya, Harmonic sum-based method for heart rate estimation using ppg signals affected with motion artifacts. J. Ambient Intell. Hum. Comput. (2016)

    Google Scholar 

  26. H. Dubey, M.R. Mehl, K. Mankodiya, BigEAR: Inferring the ambient and emotional correlates from smartphone-based acoustic big data. in IEEE International Workshop on Big Data Analytics for Smart and Connected Health (IEEE, Washington DC, USA, 2016)

    Google Scholar 

  27. H. Dubey, A. Monteiro, L. Mahler, U. Akbar, Y. Sun, Q. Yang, K. Mankodiya, FogCare: fog-assisted internet of things for smart telemedicine. Future Gener. Comput. Syst. (2016)

    Google Scholar 

  28. H. Dubey, J. Yang, N. Constant, A.M. Amiri, Q. Yang, K. Makodiya, Fog data: Enhancing telehealth big data through fog computing. in Proceedings of the ASE BigData and SocialInformatics 2015 (ACM, 2015), p. 14

    Google Scholar 

  29. K. Evangelidis, K. Ntouros, S. Makridis, C. Papatheodorou, Geospatial services in the cloud. Comput. Geosci. 63, 116–122 (2014)

    Article  Google Scholar 

  30. S. Fang, Y. Zhu, L. Xu, J. Zhang, P. Zhou, K. Luo, J. Yang, An integrated system for land resources supervision based on the iot and cloud computing. Enterprise Inf. Syst. 11(1), 105–121 (2017)

    Article  Google Scholar 

  31. J. Georis-Creuseveau, C. Claramunt, F. Gourmelon, A modelling framework for the study of spatial data infrastructures applied to coastal management and planning. Int. J. Geogr. Inf. Sci. 31(1), 122–138 (2017)

    Article  Google Scholar 

  32. G. Giuliani, P. Lacroix, Y. Guigoz, R. Roncella, L. Bigagli, M. Santoro, P. Mazzetti, S. Nativi, N. Ray, A. Lehmann, Bringing GEOSS services into practice: A capacity building resource on spatial data infrastructures (SDI). Trans. GIS 21, 811–824 (2016)

    Article  Google Scholar 

  33. C. Granell, O.B. Fernandez, L. Daz, Geospatial information infrastructures to address spatial needs in health: collaboration, challenges and opportunities. Future Gener. Comput. Syst. 31, 213–222 (2014)

    Article  Google Scholar 

  34. N. Gupta, R.K. Lenka, R.K. Barik, H. Dubey, Fair: A hadoop-based hybrid model for faculty information retrieval system. in 2017 International Conference on Intelligent Computing and Control (I2C217), IEEE, June 23–24, 2017 (IEEE, Coimbatore, India, 2017), p. 16

    Google Scholar 

  35. G.P. Hancke, G.P. Hancke Jr. et al., The role of advanced sensing in smart cities. Sensors 13(1), 393–425 (2012)

    Article  Google Scholar 

  36. L. He, P. Yue, L. Di, M. Zhang, L. Hu, Adding geospatial data provenance into SDIa service-oriented approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(2), 926–936 (2015)

    Article  Google Scholar 

  37. T. Higashino, Edge computing for cooperative real-time controls using geospatial big data. in Smart Sensors and Systems (Springer, 2017), p. 441–466

    Google Scholar 

  38. http://boundlessgeo.com/products/opengeo-suite/. Accessed 27th Jan 2017

  39. http://qgiscloud.com/rabindrabarik2016/malaria?mobile=false. Accessed 27th Jan 2017

  40. http://qgiscloud.com/rabindrabarik2016/malaria?mobile=true. Accessed 27th Jan 2017

  41. https://www.isixsigma.com/dictionary/littles-law/. Accessed 12th Jan 2017

  42. A. Jain, N. Mahajan, Introduction to database as a service. in The Cloud DBA-Oracle (Springer, 2017), p. 11–22

    Chapter  Google Scholar 

  43. H. Ji, Y. Wang, The research on the compression algorithms for vector data. in International Conference on Multimedia Technology (ICMT), 2010 (IEEE, 2010), p. 14

    Google Scholar 

  44. B. Joshi, B. Joshi, K. Rani, Mitigating data segregation and privacy issues in cloud computing. in Proceedings of International Conference on Communication and Networks (Springer, 2017), p. 175–182

    Google Scholar 

  45. H.A. Kadhim, L. Woo, S. Dlay, Novel algorithm for speech segregation by optimized kmeans of statistical properties of clustered features. in 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) (IEEE, 2015), p. 286–291

    Google Scholar 

  46. Z. Khan, D. Ludlow, R. McClatchey, A. Anjum, An architecture for integrated intelligence in urban management using cloud computing. J. Cloud Comput. Adv. Syst. Appl. 1(1), 1 (2012)

    Article  Google Scholar 

  47. S.H. Kim, S.Y. Jang, K.H. Yang, Analysis of the determinants of software-as-a-service adoption in small businesses: Risks, benefits, and organizational and environmental factors. J. Small Bus. Manag. (2016)

    Article  Google Scholar 

  48. J.G. Lee, M. Kang, Geospatial big data: challenges and opportunities. Big Data Res. 2(2), 74–81 (2015)

    Article  MathSciNet  Google Scholar 

  49. C.H. Lee, H.J. Yoon, Medical big data: promise and challenges. Kidney Res. Clin. Pract. 36(1), 3 (2017)

    Article  Google Scholar 

  50. R.K. Lenka, R.K. Barik, N. Gupta, S.M. Ali, A. Rath, H. Dubey, Comparative analysis of spatialhadoop and geospark for geospatial big data analytics. in 2nd International Conference on Contemporary Computing and Informatics (IC3I 2016) (IEEE, 2016)

    Google Scholar 

  51. Y. Ma, H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, W. Jie, Remote sensing big data computing: challenges and opportunities. Future Gener. Comput. Syst. 51, 47–60 (2015)

    Article  Google Scholar 

  52. L. Mahler, H. Dubey, C. Goldberg, K. Mankodiya, Use of smartwatch technology for people with dysarthria. in In the Proceedings of the Motor Speech Conference (Madonna Rehabilitation Hospital, 2016)

    Google Scholar 

  53. R. Mahmud, R. Buyya, Fog computing: A taxonomy, survey and future directions. arXiv preprint http://arxiv.org/abs/1611.05539arXiv:1611.05539 (2016)

  54. S. Majeed, H. Husain, S. Samad, A. Hussain, Hierarchical k-means algorithm applied on isolated malay digit speech recognition. Int. Proc. Comput. Sci. Inf. Technol. 34, 33–37 (2012)

    Google Scholar 

  55. A. Monteiro, H. Dubey, L. Mahler, Q. Yang, K. Mankodiya, Fit: A fog computing device for speech tele-treatments. in 2nd IEEE International Conference on Smart Computing (SMARTCOMP 2016) (IEEE, At Missouri, USA, 2016)

    Google Scholar 

  56. A. Munir, P. Kansakar, S.U. Khan, Ifciot: integrated fog cloud iot architectural paradigm for future internet of things. arXiv preprint http://arxiv.org/abs/1701.08474arXiv:1701.08474 (2017)

  57. S. Nunna, K. Ganesan, Mobile edge computing. in Health 4.0: How Virtualization and Big Data are Revolutionizing Healthcare (Springer, 2017), p. 187–203

    Chapter  Google Scholar 

  58. S.S. Patra, R. Barik, Dynamic dedicated server allocation for service oriented multi-agent data intensive architecture in biomedical and geospatial cloud. in Cloud Technology: Concepts, Methodologies, Tools, and Applications (IGI Global, 2015), p. 2262–2273

    Google Scholar 

  59. S. Sareen, S.K. Gupta, S.K. Sood, An intelligent and secure system for predicting and preventing zika virus outbreak using fog computing. Enterprise Inf. Syst. 121 (2017)

    Google Scholar 

  60. S. Sarkar, S. Chatterjee, S. Misra, Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans. Cloud Comput. (2015)

    Google Scholar 

  61. B. Schaffer, B. Baranski, T. Foerster, Towards spatial data infrastructures in the clouds. in Geospatial Thinking (Springer, 2010), p. 399–418

    Google Scholar 

  62. W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  63. J. Smith, W. Mackaness, A. Kealy, I. Williamson, Spatial data infrastructure requirements for mobile location based journey planning. Trans. GIS 8(1), 23–44 (2004)

    Article  Google Scholar 

  64. X. Sun, N. Ansari, EdgeIoT: mobile edge computing for the internet of things. IEEE Commun. Mag. 54(12), 22–29 (2016)

    Article  Google Scholar 

  65. B. Vanmeulebrouk, U. Rivett, A. Ricketts, M. Loudon, Open source gis for hiv/aids management. Int. J. Health Geogr. 7(1), 53 (2008)

    Article  Google Scholar 

  66. X. Wang, H. Zhang, J. Zhao, Q. Lin, Y. Zhou, J. Li, An interactive web-based analysis framework for remote sensing cloud computing. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, W2 (2015)

    Google Scholar 

  67. B. Wu, X. Wu, J. Huang, Geospatial data services within cloud computing environment. in 2010 International Conference on Audio Language and Image Processing (ICALIP) (IEEE, 2010), p. 1577–1584

    Google Scholar 

  68. C.P. Yang, Geospatial cloud computing and big data (2017). https://doi.org/10.1016/j.compenvurbsys.2016.05.001

    Article  Google Scholar 

  69. C. Yang, R. Raskin, M. Goodchild, M. Gahegan, Geospatial cyberinfrastructure: past, present and future. Comput. Environ. Urban Syst. 34(4), 264–277 (2010)

    Article  Google Scholar 

  70. C. Yang, Q. Huang, Z. Li, K. Liu, F. Hu, Big data and cloud computing: innovation opportunities and challenges. Int. J. Digit. Earth 10(1), 13–53 (2017)

    Article  Google Scholar 

  71. S. Yi, C. Li, Q. Li, A survey of fog computing: concepts, applications and issues. in Proceedings of the 2015 Workshop on Mobile Big Data (ACM, 2015), p. 37–42

    Google Scholar 

  72. J. Yu, J. Wu, M. Sarwat, Geospark: A cluster computing framework for processing largescale spatial data. in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2015), p. 70

    Google Scholar 

  73. H. Zhu, C.P. Yang, Data compression for network GIS. in Encyclopedia of GIS (Springer, 2008), p. 209–213

    Chapter  Google Scholar 

Download references

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. (#1652538). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

  1. KIIT University, Bhubaneswar, Odisha, India

    Rabindra K. Barik, Chinmaya Misra, Bhabani Shankar Prasad Mishra & Himansu Das

  2. Center for Robust Speech Systems, University of Texas at Dallas, Dallas, Texas, TX-75080, USA

    Harishchandra Dubey

  3. Wearable Biosensing Lab, University of Rhode Island, Kingston, Rhode Island, RI-02881, USA

    Debanjan Borthakur, Nicholas Constant & Kunal Mankodiya

  4. Savitribai Phule Pune University, Pune, India

    Sapana Ashok Sasane

  5. IIIT Bhubaneswar, Bhubaneswar, India

    Rakesh K. Lenka

Authors
  1. Rabindra K. Barik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harishchandra Dubey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chinmaya Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Debanjan Borthakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicholas Constant
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sapana Ashok Sasane
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rakesh K. Lenka
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bhabani Shankar Prasad Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Himansu Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kunal Mankodiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rabindra K. Barik .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  2. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Himansu Das

  3. Department of Information and Communication Technology , Fakir Mohan University, Balasore, Odisha, India

    Satchidananda Dehuri

  4. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Alok Kumar Jagadev

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Barik, R.K. et al. (2018). Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications. In: Mishra, B., Das, H., Dehuri, S., Jagadev, A. (eds) Cloud Computing for Optimization: Foundations, Applications, and Challenges. Studies in Big Data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73676-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73675-4

  • Online ISBN: 978-3-319-73676-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation