Privacy in opportunistic network contact graphs

Author(s):  
Bernhard Distl ◽  
Theus Hossmann
Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3315
Author(s):  
Aida-Ștefania Manole ◽  
Radu-Ioan Ciobanu ◽  
Ciprian Dobre ◽  
Raluca Purnichescu-Purtan

Constant Internet connectivity has become a necessity in our lives. Hence, music festival organizers allocate part of their budget for temporary Wi-Fi equipment in order to sustain the high network traffic generated in such a small geographical area, but this naturally leads to high costs that need to be decreased. Thus, in this paper, we propose a solution that can help offload some of that traffic to an opportunistic network created with the attendees’ smartphones, therefore minimizing the costs of the temporary network infrastructure. Using a music festival-based mobility model that we propose and analyze, we introduce two routing algorithms which can enable end-to-end message delivery between participants. The key factors for high performance are social metrics and limiting the number of message copies at any given time. We show that the proposed solutions are able to offer high delivery rates and low delivery delays for various scenarios at a music festival.


Author(s):  
Zakir Deniz ◽  
Esther Galby ◽  
Andrea Munaro ◽  
Bernard Ries
Keyword(s):  

2018 ◽  
pp. 280-303
Author(s):  
Anshuman Chhabra ◽  
Vidushi Vashishth ◽  
Deepak Kumar Sharma

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8058
Author(s):  
Christian E. Galarza ◽  
Jonathan M. Palma ◽  
Cecilia F. Morais ◽  
Jaime Utria ◽  
Leonardo P. Carvalho ◽  
...  

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.


2013 ◽  
Vol 13 (2) ◽  
pp. 74-82 ◽  
Author(s):  
Maggi Goyal ◽  

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