Skip to main content
SpringerLink
Log in

A machine learning-assisted data aggregation and offloading system for cloud–IoT communication

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Data aggregation and dissemination in cloud-based internet of things (IoT) are main issues because of interoperability problems in communication. In an IoT environment, data handling and offloading are constant processes that avoid communication failure and increase service utilization levels. This paper introduces a machine learning (ML)-assisted data aggregation and offloading (ML-DAO) system to improve the reliability of cloud–IoT communication. The method introduced helps reduce the response time and routing cost errors in data aggregation and improve the data service rate. The data handling rate is also enhanced using the IoT assisted by fog elements that maximize edge-level communication. Cloud–IoT communication quality is measured on the basis of time and service attributes; ML techniques are designed to enhance the level’s precision while aggregating the data. To achieve optimum communication quality, the proposed ML-DAO operates on certain measurable functional metrics. The performance of the system is assessed using the following metrics: route cost error, processing time, aggregation delay, service utilization rate, failure probability, and response time. Experimental results prove the consistency of the proposed scheme, as the metrics are optimized with lesser unallocated data chunks.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Alavi AH, Jiao P, Buttlar WG, Lajnef N (2018) Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement 129:589–606

    Article  Google Scholar 

  2. AlFarraj O, Tolba AAlZubi,A (2019) Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics. Neural Comput Appl 31(5):1391–1403

    Article  Google Scholar 

  3. Sicari S, Rizzardi A, Miorandi D, Coen-Porisini A (2018) A risk assessment methodology for the internet of things. Comput Commun 129:67–79

    Article  Google Scholar 

  4. Fouad H, Mahmoud NM, El Issawi MS, Al-Feel H (2020) Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system. Comput Commun 151:257–265

    Article  Google Scholar 

  5. Haw R, Alarm M, Hong C (2014) A context-aware content delivery framework for QoS in mobile cloud. Proc. IEEE NOMS, pp 1–6

  6. Al-Makhadmeh Z, Tolba A (2020) SRAF: Scalable Resource Allocation Framework using machine learning in user-centric internet of things. Peer-to-peer networking and applications. https://doi.org/10.1007/s12083-020-00924-3

  7. Sheron PF, Sridhar KP, Baskar S, Shakeel PM (2019) A decentralized scalable security framework for end-to‐end authentication of future IoT communication. Transactions on Emerging Telecommunications Technologies, e3815. https://doi.org/10.1002/ett.3815

  8. Mubarakali A, Durai AD, Alshehri M, AlFarraj O, Ramakrishnan J, Mavaluru D (2020) Fog-based delay-sensitive data transmission algorithm for data forwarding and storage in cloud environment for multimedia applications. Big Data. https://doi.org/10.1089/big.2020.0090

    Article  Google Scholar 

  9. Baskar S, Periyanayagi S, Shakeel PM, Dhulipala VS (2019) An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput Netw 152:144–153. https://doi.org/10.1016/j.comnet.2019.01.027

    Article  Google Scholar 

  10. Said O, Al-Makhadmeh Z, Tolba A (2020) EMS: An energy management scheme for green IoT environments. IEEE Access 8:44983–44998

    Article  Google Scholar 

  11. Oteafy SMA, Hassanein HS (2018) IoT in the fog: A roadmap for data-centric IoT development. IEEE Commun Mag 56(3):157–163

    Article  Google Scholar 

  12. Wang J, Tang Y, He S, Zhao C, Sharma PK, Alfarraj O, Tolba A (2020) LogEvent2vec: logEvent-to-vector based anomaly detection for large-scale logs in internet of things. Sensors 20(9):2451

    Article  Google Scholar 

  13. Naha RK, Garg S, Georgakopoulos D, Jayaraman PP, Gao L, Xiang Y, Ranjan R (2018) Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access 6:47980–48009

    Article  Google Scholar 

  14. Alsiddiky A, Awwad W, Fouad H, Hassanein AS, Soliman AM (2020) Priority-based data transmission using selective decision modes in wearable sensor based healthcare applications. Comput Commun 160:43–51

    Article  Google Scholar 

  15. Li H, Ota K, Dong M (2018) Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Network 32(1):96–101

    Article  Google Scholar 

  16. Rahim A, Ma K, Zhao W, Tolba A, Al-Makhadmeh Z, Xia F (2018) Cooperative data forwarding based on crowdsourcing in vehicular social networks. Pervasive Mob Comput 51:43–55

    Article  Google Scholar 

  17. Ji H, Alfarraj O, Tolba A (2020) Artificial intelligence-empowered edge of vehicles: architecture, enabling technologies, and applications. IEEE Access 8:61020–61034

    Article  Google Scholar 

  18. Tolba A, Al-Makhadmeh Z (2020) A recursive learning technique for improving information processing through message classification in IoT–cloud storage. Comput Commun 150:719–728

    Article  Google Scholar 

  19. Kato N et al (2017) The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE Wirel Commun 24(3):146–53.  https://doi.org/10.1109/MWC.2016.1600317WC

  20. AlFarraj O, Tolba A, Alkhalaf S, AlZubi A (2019) Neighbor predictive adaptive handoff algorithm for improving mobility management in VANETs. Comput Netw 151:224–231

    Article  Google Scholar 

  21. Kayes ASM, Rahayu W, Dillon T (2018) Critical situation management utilizing IoT-based data resources through dynamic contextual role modeling and activation. Computing

  22. Kim J, Jeon Y, Kim H (2016) The intelligent IoT common service platform architecture and service implementation. J Supercomput 74(9):4242–4260

    Article  Google Scholar 

  23. Ullah F, Wang J, Farhan M, Jabbar S, Naseer MK, Asif M (2018) LSA based smart assessment methodology for SDN infrastructure in IoT environment. Int J Parallel Program

  24. Puschmann D, Barnaghi P, Tafazolli R (2016) Adaptive clustering for dynamic IoT data streams. IEEE Internet Things J :1–1

  25. Xiao W, Bao W, Zhu X, Liu L (2017) Cost-aware big data processing across geo-distributed datacenters. IEEE Trans Parallel Distrib Syst 28(11):3114–3127

    Article  Google Scholar 

  26. Lu Z, Wang N, Wu J, Qiu M (2018) IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds. J Parallel Distrib Comput 118:316–327

    Article  Google Scholar 

  27. Bu F (2018) An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT. Future Gener Comput Syst 88:675–682

    Article  Google Scholar 

  28. Cheng B, Solmaz G, Cirillo F, Kovacs E, Terasawa K, Kitazawa A (2018) FogFlow: Easy programming of IoT services over cloud and edges for smart cities. IEEE Internet Things J 5(2):696–707

    Article  Google Scholar 

  29. Yacchirema DC, Sarabia-Jacome D, Palau CE, Esteve M (2018) A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6:35988–36001

    Article  Google Scholar 

  30. Zhao W, Liu J, Guo H, Hara T (2018) Edge-node-assisted transmitting for the cloud-centric internet of things. IEEE Netw 32(3):101–107

    Article  Google Scholar 

  31. He J, Wei J, Chen K, Tang Z, Zhou Y, Zhang Y (2018) Multitier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet Things J 5(2):677–686

    Article  Google Scholar 

  32. Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2017) iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things. Edge and Fog computing environments. Softw Pract Experience 47(9):1275–1296

Download references

Acknowledgements

This work is funded by the Researchers Supporting Project No. (RSP-2020/102) King Saud University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. Computer Science Department, Community College, King Saud University, 11437, Riyadh, Saudi Arabia

    Osama Alfarraj

Authors
  1. Osama Alfarraj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Osama Alfarraj.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications

Guest Editor: Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alfarraj, O. A machine learning-assisted data aggregation and offloading system for cloud–IoT communication. Peer-to-Peer Netw. Appl. 14, 2554–2564 (2021). https://doi.org/10.1007/s12083-020-01014-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-01014-0

Keywords

Search

Navigation