Advances in Communication Networking: 19th EUNICE/IFIP WG by Uwe Bauknecht, Frank Feller (auth.), Thomas Bauschert (eds.)

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By Uwe Bauknecht, Frank Feller (auth.), Thomas Bauschert (eds.)

This booklet constitutes the refereed court cases of the nineteenth EUNICE/IFIP WG 6.2, 6.6 workshop on Advances in conversation Networking, EUNICE 2013, held in Chemnitz, Germany, in August 2013.

The 23 oral papers validated including nine poster shows have been conscientiously reviewed and chosen from forty submissions. The papers are prepared in topical sections on community modeling and layout, site visitors research, community and site visitors administration, providers over cellular networks, tracking and dimension, safeguard techniques, program of ICT in shrewdpermanent grid and clever domestic environments, information dissemination in ad-hoc and sensor networks, and providers and applications.

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Additional resources for Advances in Communication Networking: 19th EUNICE/IFIP WG 6.6 International Workshop, Chemnitz, Germany, August 28-30, 2013. Proceedings

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In: 2010 6th EURO-NF Conference on Next Generation Internet (NGI), pp. 1–8. IEEE (2010) 14. : BMBF project ROBUKOM: Robust communication networks. In: Euro View 2012, Berlin, Offenbach. VDE-Verlag (2012) Modeling and Quantifying the Survivability of Telecommunication Network Systems under Fault Propagation Lang Xie, Poul E. no Abstract. This paper presents a generic state transition model to quantify the survivability attributes of a telecommunication network under fault propagation. This model provides a framework to characterize the network performance during the transient period that starts after the fault occurrence, in the subsequent fault propagation, and until the network fully recovers.

Comparison of the fraction of connected users in model I and II for different values of fault propagation rates Then the percentage of connected users is equal to the fraction of available base stations. The rewards at each model state are shown in Table 2. First, we compare model I and model II in this network example for different values of fault propagation rates. In fig. 67, there is a gap between model I and model II curves. Compared to model I, the fraction of active users in model II decreases more sharply.

R d∈I ,d,n ≤ 1 ∀ ∈ L, ∀n ∈ N (19) ,n All bounds and limitations of the optimization variables are listed in (20). 3 ∀ ∀ ∀ ∀ ∀ ∀ ∀ ∀ ∀ ∀ ∈L ∈ L, ∀s ∈ L, ∀j ∈ Es , ∀p ∈ Ps,j max ∈ Lmax , ∀ ∈ L, ∀j ∈ E , ∀p ∈ P ,j ∈ L, ∀e ∈ E ∈ L, ∀s ∈ L , ∀n ∈ N ∈ L, ∀n ∈ N ∈ L, ∀n ∈ N ∈ L, ∀n ∈ N , ∀d ∈ I ,n ∈ L, ∀j ∈ E max , ∀p ∈ P ,j : e ∈ p ∈ L\ { max } , ∀n ∈ N max , ∀j ∈ δ (n) , ∀p ∈ P (20) ,j MILP Size Estimation In order to compare the complexity of the non-robust model with the model that includes Γ -robustness, we perform an estimation of the model sizes.

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