Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Wireless Networks
Erschienen in: Wireless Networks 6/2013

01.08.2013

A joint parametric prediction model for wireless internet traffic using Hidden Markov Model

verfasst von: Sumit Maheshwari, Sudipta Mahapatra, C. S. Kumar, K. Vasu

Erschienen in: Wireless Networks | Ausgabe 6/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Addressing performance related issues of networks and ensuring better Quality of Service (QoS) for end-users calls for simple, tractable and realistic traffic models. The work reported here focuses on modelling the Wireless Internet traffic using realistic traffic traces collected over wireless networks and forecasting the end-to-end QoS parameters for the networks. A measurement framework is set-up to collect the QoS parameters and a traffic model is designed based on Hidden Markov Model considering joint distribution of End to End Delay (E2ED or d), Inter-Packet Delay Variation (IPDV) and Packet Size. States are mapped to the four traffic classes namely conversational, streaming, interactive, and background. The model is validated by forecasting QoS parameters and the results are shown to be within the tolerance limit.
Vorheriger Artikel SAS-TDMA: a source aware scheduling algorithm for real-time communication in industrial wireless sensor networks
Nächster Artikel Collision avoidance slot allocation scheme for multi-cluster wireless sensor networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Akyildiz, I. F., Xie, J., & Mohanty, S. (2004). A survey of mobility management in next-generation all-IP-based wireless systems. IEEE Wireless Communication, 11(4), 16–28.CrossRef
2.
Larissa O. Ostrowsky, Nelson L. S. da Fonseca and Cesar A. V. Melo, “A Traffic Model for UDP Flows”, in the proc. IEEE Communications Society, ICC 2007.
3.
Math, B. A. (1997). “An empirical model of HTTP network traffic”, in Proceedings. IEEE INFOCOM, 2, 592–600.
4.
Feldmann A., Rexford J., “IP network configuration for traffic engineering”, AT&T Labs—Research, Tech. Rep. 000526-02, May 2000.
5.
Feldmann, A., Greenberg, A., Lund, C., Reingold, N., & Rexford, J. (2000). NetScope: Traffic engineering for IP networks (pp. 11–19). Mar: IEEE Network Magazine.
6.
Breslau, L., Cao, P., Fan, L., Philips, G., & Shenker, S. (1999). Web caching and Zipf like distributions: Evidence and implications. Proceedings of IEEE INFOCOM, 1, 126–134.
7.
Lee, I. W. C., & Fapojuwo, A. O. (2009). Analysis and modelling of a campus wireless network TCP/IP traffic. Computer Networks: The International Journal of Computer and Telecommunications Networking, 53(15), 2674–2687.MATH
8.
Lakkakorpi, J., Heiner, A., Ruutu, J. (2002) “Measurement and characterization of Internet gaming traffic,” Research Seminar on Networking, Helsinki University of Technology, Networking Laboratory, Espoo, Finland.
9.
Estan, C., & Varghese, G. (2003). New directions in traffic measurement and accounting: focusing on the elephants, ignoring the mice. ACM Transactions on Computer Systems, 21(3), 270–313.CrossRef
10.
Williamson, C. (2001). Internet Traffic Measurement. IEEE Internet Computing, 5, 70–74.CrossRef
11.
Irnich, T., & Stuckmann, P. (2003). “Fluid-flow modelling of Internet traffic in GSM/GPRS networks”, in. Computer Communications, 26, 1756–1763.CrossRef
12.
Heyman, D., Lucantoni, D. (2001) “Modelling multiple IP traffic streams with rate limits”, in Proceeding 17th International Teletraffic Congress, Brazil.
13.
Steven L. Scott and Padhraic Smyth (2003). “The Markov Modulated Poisson Process and Markov Poisson Cascade with Applications to Web Traffic Modelling”, Oxford University Press.
14.
Al-Akaidi, M., Alani, O. (3G 2004). “Analytical model for multimedia traffic in 3G wireless systems”, Fifth IEE International Conference on 3G Mobile Communication Technologies.
15.
Bing Wang et al. (2008). “Multimedia Streaming via TCP: An Analytic Performance Study”, ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 4, No. 2, Article 16.
16.
Hassam, H., Garcia, J. M., Bockstal, C., “Aggregate Traffic Models for VoIP Applications”, in Proc. International conference on Digital Telecommunications.
17.
Casilari, E., González, F. J., Sandoval, F. (2001). “Modelling of HTTP Traffic”, IEEE Communications Letters, Vol. 5, No. 6.
18.
Dainotti, A., Pescap′e, A., Rossi, P. S., Palmieri, F., & Ventre, G. (2008). “Internet traffic modelling by means of hidden markov models”. Computer Networks, 52(14), 2645–2662.MATHCrossRef
19.
Wright, C.V., Monrose, F., Masson, G.M. (2004). “HMM profiles for network traffic classification (extended abstract)”, In: Proceeding ACM Workshop on Visualization and Data Mining for Computer Security, pp. 9.
20.
Costamagna, E., Favalli, L., Tarantola, F. (2003). “Modelling and analysis aggregate and single stream Internet traffic”, in: Proc. IEEE GLOBECOM, pp. 3830–3834.
21.
22.
Sumit. M., Vasu, K., Sudipta, M., Kumar C. S. (2011). “A Joint-Parametric Realistic Traffic Model for Wireless Internet using Hidden Markov Model”, in CSQRWC, vol. 1, pp. 806–811.
23.
Sumit, M., Vasu, K., Kumar, C. S., Sudipta, M. (2011). “Measurement and Comparative Analysis of UDP Traffic over Wireless Networks”, in ICWN, pp. 258–262, Las Vegas, USA.
24.
Sumit, M., Vasu, K., Kumar, C. S., Sudipta, M. (2010).“Measurement and Analysis of UDP Traffic over Wi-Fi and GPRS”, in ICCCD, IIT Kharagpur, India.
25.
Pasztor A., Veitch D. (2002). “Active Probing using Packet Quartets,” in Proc. Internet Measurement Workshop.
26.
27.
Chlebus, E., & Brazier, J. (2007). “Nonstationary Poisson modelling of web browsing session arrivals”, in. Information Processing Letters, 102, 187–190.MathSciNetMATHCrossRef
28.
Lehr, W., McKnight, L. (2002). “Wireless Internet Access: 3G vs. WiFi?”, Telecommunication Policy, pp. 351–370.
29.
Bilmes J. (1997). “A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden markov models”, Technical Report ICSI-TR-97-02, University of Berkeley.
Metadaten
Titel
A joint parametric prediction model for wireless internet traffic using Hidden Markov Model
verfasst von
Sumit Maheshwari
Sudipta Mahapatra
C. S. Kumar
K. Vasu
Publikationsdatum
01.08.2013
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 6/2013
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-012-0525-1

Weitere Artikel der Ausgabe 6/2013

Wireless Networks 6/2013 Zur Ausgabe

OriginalPaper

Neighborhood prediction based decentralized key management for mobile wireless networks

OriginalPaper

Band-based geocasting for mobile sink groups in wireless sensor networks

OriginalPaper

Toward a secure batch verification with group testing for VANET

OriginalPaper

EHM: a novel efficient protocol based handshaking mechanism for underwater acoustic sensor networks

OriginalPaper

Collision avoidance slot allocation scheme for multi-cluster wireless sensor networks

OriginalPaper

Optimization of adaptive antenna system parameters in self-organizing LTE networks

Neuer Inhalt

    Bildnachweise