scholarly journals Protegendo Redes de Canais de Pagamento Sem Fio com Janelas de Tempo de Bloqueio Mínimas

2021 ◽  
Author(s):  
Gabriel Antonio F. Rebello ◽  
Maria Potop-Butucaru ◽  
Marcelo Dias de Amorim ◽  
Otto Carlos M. B. Duarte
Keyword(s):  

Redes de canais de pagamento (Payment Channel Networks - PCN) aumentam o impacto das criptomoedas, fornecendo uma solução rápida e independente de consenso para mitigar os problemas de escalabilidade dos protocolos tradicionais de correntes de blocos (blockchain). No entanto, as PCNs atuais são baseadas em nós robustos com alta disponibilidade e capacidade computacional, dificultando sua adoção em ambientes móveis e sem fio. Este artigo propõe uma arquitetura PCN híbrida que estende as funcionalidades das PCNs tradicionais para cenários de dispositivos sem fio com recursos limitados. O artigo analisa a vulnerabilidade de roubo de tokens e propõe uma contramedida com base em janelas de tempo de bloqueio. O artigo avalia a proposta com dados reais da Lightning Network e de redes de banda larga móvel. Os resultados mostram que a janela de tempo mínimo de bloqueio depende do tempo de inatividade dos dispositivos e que selecionar uma janela padrão é mais eficaz quando os dispositivos apresentam alta disponibilidade.

1998 ◽  
Vol 26 (1) ◽  
pp. 289-327 ◽  
Author(s):  
Andrea Rinaldo ◽  
Ignacio Rodriguez-Iturbe ◽  
Riccardo Rigon
Keyword(s):  

1980 ◽  
Vol 1 (1) ◽  
pp. 25-33 ◽  
Author(s):  
A. Meir ◽  
J. W. Moon ◽  
J. R. Pounder
Keyword(s):  

1996 ◽  
Vol 187 (1-2) ◽  
pp. 137-144 ◽  
Author(s):  
M. Veltri ◽  
P. Veltri ◽  
M. Maiolo

Author(s):  
Peter Bräuer ◽  
Siegfried Fritzsche ◽  
Jörg Kärger ◽  
Gunter Schütz ◽  
Sergey Vasenkov
Keyword(s):  

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Majid Niazkar ◽  
Farshad Hajizadeh mishi ◽  
Gökçen Eryılmaz Türkkan

The study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs.


2004 ◽  
Vol 27 (7) ◽  
pp. 781-802 ◽  
Author(s):  
Martin Gugat ◽  
Günter Leugering ◽  
E. J. P. Georg Schmidt

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