A dense memory representation using bitmap data structure for improving NDN push-traffic model | Annals of Telecommunications Skip to main content
Springer Nature Link
Log in

A dense memory representation using bitmap data structure for improving NDN push-traffic model

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

The exponential growth of the Internet demands in return new technologies and protocols that can handle the new requirements of such growth efficiently. Such developments have enabled and offered many new services with sophisticated requirements that go beyond the TCP/IP host-centric model capabilities and increase its complexity. Researchers have proposed new architecture called Named-Data Networking (NDN) for Information-Centric Networking (ICN) based on a strict pull-based model as an alternative option to TCP/IP. This model has gained significant attention in the research field. However, this model still suffers from the looped data redundancy problem, which may lead to frequent link failures when dealing with real-time streaming due to the persistent interest packets. In this paper, a push-based model along with a bitmap algorithm has been proposed for improving the ICN efficiency by eliminating such problems. The presented model involved extensive experimental simulations. The experimental results demonstrate the model feasibility by preventing most of the data redundancy and improving the harmonic rein of frequent link failures respectively.

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

Similar content being viewed by others

Data Availability

The data that support the findings of this study are openly available and obtained from persistent intersest software in GitHub (www.github.com/phylib/PersistentInterest)

References

  1. Xylomenos G, Ververidis CN, Siris VA, Fotiou N, Tsilopoulos C, Vasilakos X, Katsaros KV, Polyzos GC (2013) A survey of information-centric networking research. IEEE Commun Surv Tutor 16(2):1024–1049

    Article  Google Scholar 

  2. Yao H, Li M, Du J, Zhang P, Jiang C, Han Z (2019) Artificial intelligence for information-centric networks. IEEE Commun Mag 57(6):47–53

    Article  Google Scholar 

  3. Naeem MA, Nor SA, Hassan S, Kim B-S (2018) Performances of probabilistic caching strategies in content centric networking. IEEE Access 6:58807–58825

    Article  Google Scholar 

  4. De Brito GM, Velloso PB, Moraes IM (2013) Information-centric networks: a new paradigm for the internet. John Wiley & Sons

    Book  Google Scholar 

  5. Vasilakos AV, Li Z, Simon G, You W (2015) Information centric network: Research challenges and opportunities. J Netw Comput Appl 52:1–10

    Article  Google Scholar 

  6. Yu K, Arifuzzaman M, Wen Z, Zhang D, Sato T (2015) A key management scheme for secure communications of information centric advanced metering infrastructure in smart grid. IEEE Trans Instrum Meas 64(8):2072–2085

    Article  Google Scholar 

  7. Samain J, Carofiglio G, Muscariello L, Papalini M, Sardara M, Tortelli M, Rossi D (2017) Dynamic adaptive video streaming: Towards a systematic comparison of ICN and TCP/IP. IEEE Trans Multimed 19(10):2166–2181

    Article  Google Scholar 

  8. Shen Z, Zhang T, Jin J, Yokota K, Tagami A, Higashino T (2019) ICCF: An information-centric collaborative fog platform for building energy management systems. IEEE Access 7:40402–40415

    Article  Google Scholar 

  9. Zhang L, Estrin D, Burke J, Jacobson V, Thornton JD, Smetters DK, Zhang B, Tsudik G, Massey D, Papadopoulos C et al (2010) Relatório Técnico NDN-0001. Xerox Palo Alto Research Center-PARC 157:158

    Google Scholar 

  10. Saxena D, Raychoudhury V, Suri N, Becker C, Cao J (2016) Named data networking: a survey. Comput Sci Rev 19:15–55

    Article  MathSciNet  Google Scholar 

  11. Li Q, Lee PP, Zhang P, Su P, He L, Ren K (2017) Capability-based security enforcement in named data networking. IEEE/ACM Trans Netw 25(5):2719–2730

    Article  Google Scholar 

  12. Yu Y, Li Y, Du X, Chen R, Yang B (2018) Content protection in named data networking: Challenges and potential solutions. IEEE Commun Mag 56(11):82–87

    Article  Google Scholar 

  13. Bourtsoulatze E, Thomos N, Saltarin J, Braun T (2017) Content-aware delivery of scalable video in network coding enabled named data networks. IEEE Trans Multimed 20(6):1561–1575

    Article  Google Scholar 

  14. Jacobson V, Smetters DK, Briggs NH, Plass MF, Stewart P, Thornton JD, Braynard RL (2009) VoCCN: voice-over content-centric networks. In: Proceedings of the 2009 workshop on Re-architecting the internet, pp 1–6

  15. Zhu Z, Wang S, Yang X, Jacobson V, Zhang L (2011) ACT: audio conference tool over named data networking. In: Proceedings of the ACM SIGCOMM workshop on Information-centric networking, pp 68–73

  16. Gusev P, Burke J (2015) Ndn-rtc: Real-time videoconferencing over named data networking. In: Proceedings of the 2nd ACM Xonference on Information-Centric Networking, pp 117–126

  17. Gusev P, Wang Z, Burke J, Zhang L, Yoneda T, Ohnishi R, Muramoto E (2016) Real-time streaming data delivery over named data networking. IEICE Trans Commun 99(5):974–991

    Article  Google Scholar 

  18. Phanama YA, Ekadiyanto FA, Sari RF (2019) Evaluation of Parameters Affecting the Performance of Real Time Streaming on Real Time Communication Library in Named Data Networking. In: Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), vol 1, pp 203–220. Springer International Publishing

  19. Yao C, Fan L, Yan Z, Xiang Y (2012) Long-term interest for realtime applications in the named data network. Proceedings of ACM AsiaFI12, pp 1–8

  20. Tsilopoulos C, Xylomenos G (2011) Supporting diverse traffic types in information centric networks. In: Proceedings of the ACM SIGCOMM workshop on Information-centric networking, pp 13–18

  21. Posch D, Rainer B, Hellwagner H (2016) SAF: Stochastic adaptive forwarding in named data networking. IEEE/ACM Trans Netw 25(2):1089–1102

    Article  Google Scholar 

  22. Moll P, Janda J, Hellwagner H (2017) Adaptive forwarding of persistent interests in named data networking. In: Proceedings of the 4th ACM Conference on Information-Centric Networking, pp 180–181

  23. Yi C, Afanasyev A, Moiseenko I, Wang L, Zhang B, Zhang L (2013) A case for stateful forwarding plane. Comput Commun 36(7):779–791

  24. Yi C, Abraham J, Afanasyev A, Wang L, Zhang B, Zhang L (2014) On the role of routing in named data networking. In: Proceedings of the 1st ACM conference on information-centric networking, pp 27–36

  25. Banerjee S, Naskar T, Biswash SK (2020) The survey, research challenges, and opportunities in ICN. Cloud Netw Manag 26:27–45

  26. Moll P, Posch D, Hellwagner H (2017) Investigation of push-based traffic for conversational services in named data networking. In: 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp 315–320. IEEE

  27. Moll P, Theuermann S, Hellwagner H (2018) Persistent interests in named data networking. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp 1–5. IEEE

  28. Amadeo M, Ruggeri G, Campolo C, Molinaro A (2019a) IoT services allocation at the edge via named data networking: From optimal bounds to practical design. IEEE Trans Netw Serv Manag 16(2):661–674

    Article  Google Scholar 

  29. Amadeo M, Ruggeri G, Campolo C, Molinaro A, Loscrí V, Calafate CT (2019b) Fog computing in IoT smart environments via named data networking: A study on service orchestration mechanisms. Futur Internet 11(11):222

    Article  Google Scholar 

  30. Gameiro L, Senna C, Luís M (2020) NdnIoT-FC: IoT devices as first-class traffic in name data networks. Futur Internet 12(11):207

    Article  Google Scholar 

  31. Saxena D, Raychoudhury V, Becker C (2017) Implementation and performance evaluation of name-based forwarding schemes in VNDN. In: Proceedings of the 18th International Conference on Distributed Computing and Networking, pp 1–4

  32. Ghasemi C, Yousefi H, Zhang B (2020) Icdn: An ndn-based cdn. In: Proceedings of the 7th ACM Conference on Information-Centric Networking, pp 99–105

  33. Garcia-Luna-Aceves JJ, Mirzazad-Barijough M (2016) A light-weight forwarding plane for content-centric networks. In: 2016 International Conference on Computing, Networking and Communications (ICNC), pp 1–7. IEEE

  34. Garcia-Luna-Aceves JJ, Barijough MM (2017) Efficient multicasting in content-centric networks using locator-based forwarding state. In: 2017 International Conference on Computing, Networking and Communications (ICNC), pp 172–177. IEEE

  35. Hu X, Liu X, Zhao L, Gong J, Cheng G (2019) Enhancing interest forwarding for fast recovery from unanticipated data access failure in NDN. China Commun 16(7):120–130

    Article  Google Scholar 

  36. Chowdhury M, Khan JA, Wang L (2020) Leveraging content connectivity and location awareness for adaptive forwarding in NDNbased mobile ad hoc networks. In: Proceedings of the 7th ACM Conference on Information-Centric Networking, pp 59–69

  37. Nikzad M, Jamshidi K, Bohlooli A (2020) A responsibility-based transport control for named data networking. Futur Gener Comput Syst 106:518–533

    Article  Google Scholar 

  38. Mastorakis S, Afanasyev A, Moiseenko I, Zhang L (2016) ndnSIM 2: An updated NDN simulator for NS-3. NDN, Technical Report NDN-0028, Revision 2

Download references

Author information

Authors and Affiliations

  1. Department of Computer Networks and Distributed System, Taiz University, Taiz, Yemen

    Amer Sallam, Noran Aklan & Norhan Aklan

  2. School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

    Taha H. Rassem

Authors
  1. Amer Sallam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noran Aklan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Norhan Aklan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taha H. Rassem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taha H. Rassem.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sallam, A., Aklan, N., Aklan, N. et al. A dense memory representation using bitmap data structure for improving NDN push-traffic model. Ann. Telecommun. 79, 73–83 (2024). https://doi.org/10.1007/s12243-023-00972-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-023-00972-9

Keywords

Search

Navigation