A distributed multi-layer MEC-cloud architecture for processing large scale IoT-based multimedia applications | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

A distributed multi-layer MEC-cloud architecture for processing large scale IoT-based multimedia applications

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The enormous growth of the Internet of Things (IoT) devices gave governments, businesses, and individual users new means to accomplish their missions. Several IoT applications require deployment at a large scale such as smart cities. Large-scale IoT applications help achieve better monitoring services and more efficient cities. IoT multimedia (IoTMM) applications provide a unique level of intelligence that cannot be obtained with traditional IoT applications. IoTMM applications intensely consume bandwidth, processing, and storage resources compared to traditional IoT applications. IoT nodes vary in their capabilities, where some nodes have high processing capabilities while others do not. Cloud services models (IaaS, PaaS, and SaaS) provide a perfect solution for applications of high demand for resources. However, such models are inefficient when dealing with multimedia applications due to the high bandwidth requirements before reaching the cloud. Mobile edge computing (MEC) model creates a new level of providers according to the proximity of the end users to the network resources. Edge providers can offload some processing that is usually done at the cloud, which improves the overall performance of cloud-based applications. In this paper, we propose a new distributed structure for processing multimedia applications in the cloud, where different layers of processing and providers are involved. Every layer is responsible for a specific role depending on the type of the multimedia application and multimedia device. The major contribution of the proposed architecture includes two main elements: the support of scalable IoTMM applications in large deployment scenarios and the support of effective multimedia information sharing. The proposed architecture supports a scalable architecture for IoTMM applications with minimum additional resources compared to the traditional models. The proposed model allows for effective and practical sharing of multimedia information (raw multimedia data or features extracted from multimedia data). In addition, the proposed architecture provides applications with the ability of accessing intelligent multimedia information and services with minimum software development efforts. We support our claims with detailed simulation results and analysis.

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
¥17,985 /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 includes VAT (Japan)

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. Almajali S, Abou-Tair DDI (2017) Cloud based intelligent extensible shared context services. In: 2017 Second international conference on fog and mobile edge computing (FMEC), Valencia, Spain, pp 133–138

  2. Almajali S, Salameh HB, Ayyash M, Elgala H (2018) A framework for efficient and secured mobility of IoT devices in mobile edge computing. In: 2018 Second International Conference on Fog and Mobile Edge Computing (FMEC), Barcelona, Spain, pp 58–62

  3. Alvi Sheeraz A, Afzal B, Shah Ghalib A, Atzori L, Mahmood W (2015) Internet of multimedia things: Vision and challenges. Ad Hoc Netw 33:87–111

    Article  Google Scholar 

  4. Anagnostopoulos C, Hadjiefthymiades S, Zervas E (2011) Information dissemination between mobile nodes for collaborative context awareness. IEEE Trans Mob Comput 10(12):1710–1725

    Article  MATH  Google Scholar 

  5. Anagnostopoulos C, Zervas E, Hadjiefthymiades S (2007) An epidemiological model for semantics dissemination. In: MobiMedia ’07:10:1–10:6ICST (institute for computer sciences social-informatics and telecommunications engineering); ICST, Brussels, Belgium

  6. Assuncao Marcos D, Netto Marco AS, Koch F, Bianchi S (2012) Context-aware job scheduling for cloud computing environments. In: UCC ’12:255–262IEEE computer society, Washington, DC, USA

  7. Badidi E, Taleb I (2011) Towards a cloud-based framework for context management. In: 2011 international conference on innovations in information technology, Abu Dhabi, United Arab Emirates, pp 35–40

  8. Balan T, Robu D, Sandu F (2017) Multihoming for mobile internet of multimedia things. Mob Inf Syst 2017:1–16

    Google Scholar 

  9. Bolchini C, Curino C, Schreiber FA, Tanca L (2006) Context integration for mobile data tailoring. In: 7th International conference on mobile data management (MDM’06), Nara, Japan, pp 5–5

  10. Forkan A, Khalil I, Tari Z (2014) CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Gener Comput Syst 35:114–127

    Article  Google Scholar 

  11. Grasa E, Leon MP, Meer S, Lopez D, Tarzan M (2017) Open multi-access edge computing and distributed mobility management with RINA. In: 2017 IEEE conference on network function virtualization and software defined networks (NFV-SDN), Berlin, Germany, pp 1–2

  12. La HJ, Kim SD (2010) A conceptual framework for provisioning context-aware mobile cloud services. In: CLOUD ’10:466–473IEEE computer society, Washington, DC, USA

  13. Long W, Yuqing L, Qingxin X (2013) Using cloudsim to model and simulate cloud computing environment. In: 2013 Ninth international conference on computational intelligence and security. IEEE, Leshan, pp 323–328

  14. Min J-K, Cho S-B (2012) Semantic management of multiple contexts in a pervasive computing framework. Expert Syst Appl 39(10):8655–8664

    Article  Google Scholar 

  15. Moore P, Xhafa F, Barolli L (2014) Context-as-a-service: A service model for Cloud-Based systems. In: CISIS ’14:379–385IEEE computer society, Washington, DC, USA 509

  16. Mowafi Y, Abou-Tair D, Aqarbeh T, Abilov M, Dmitriyev V, Gomez JM (2014) A context-aware adaptive security framework for mobile applications. In: ICCASA ’14:147–153ICST (Institute for Computer Sciences Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium

  17. Nordrum A (2016) The internet of fewer things [News]. IEEE Spectrum 53(10):12–13

    Article  Google Scholar 

  18. Okwuibe J, Liyanage M, Ahmad I, Ylianttila M (2018) Cloud and MEC Security. In: John Wiley & Sons, Ltd, pp 373–397

  19. Otebolaku AM, Andrade MT (2014) Supporting context-aware cloud-based media recommendations for smartphones. In: MOBILECLOUD ’14:109–116 IEEE computer society, Washington, DC, USA

  20. Pyattaev A, Johnsson K, Andreev S, Koucheryavy Y (2013) 3GPP LTE traffic offloading onto WiFi Direct. In: 2013 IEEE wireless communications and networking conference workshops (WCNCW), Shanghai, pp 135–140

  21. Said O, Albagory Y, Nofal M, Raddady FA (2017) IoT-RTP and IoT-RTCP: adaptive protocols for multimedia transmission over internet of things environments. IEEE Access 5:16757–16773

    Article  Google Scholar 

  22. Salameh HAB, Almajali S, Ayyash M, Elgala H (2018) Spectrum assignment in cognitive radio networks for internet-of-things delay-sensitive applications under jamming attacks. IEEE Internet Things J 5(3):1904–1913

    Article  Google Scholar 

  23. Wang Q, Zhao Y, Wang W et al (2017) Multimedia IoT systems and applications. In: 2017 global internet of things summit (GIoTS), Geneva, Switzerland, pp 1–6

  24. Wang W, Wang Q, Sohraby K (2016) Multimedia sensing as a service (MSaaS): Exploring resource saving potentials of at cloud-edge IoTs and fogs. IEEE Internet Things J 4(2):487–495

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Princess Sumaya University for Technology, Amman, Jordan

    Sufyan Almajali

  2. Department of Computer Science, German-Jordanian University, Amman, Jordan

    Dhiah el Diehn I. Abou-Tair

  3. Department of Telecommunications Engineering, Yarmouk university, Irbid, Jordan

    Haythem Bany Salameh

  4. Department of Information Studies, Chicago State University, Chicago, IL, 60628, USA

    Moussa Ayyash

  5. Department of Electrical Engineering, University at Albany-SUNY, Albany, NY, 12222, USA

    Hany Elgala

Authors
  1. Sufyan Almajali
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dhiah el Diehn I. Abou-Tair
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haythem Bany Salameh
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Moussa Ayyash
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Hany Elgala
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Haythem Bany Salameh.

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

Almajali, S., Abou-Tair, D.e.D.I., Salameh, H.B. et al. A distributed multi-layer MEC-cloud architecture for processing large scale IoT-based multimedia applications. Multimed Tools Appl 78, 24617–24638 (2019). https://doi.org/10.1007/s11042-018-7049-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7049-3

Keywords