High-fidelity application-centric evaluation framework for vehicular networks

Author(s):  
Yi Yang ◽  
Maneesh Varshney ◽  
Shrinivas Mohan ◽  
Rajive Bagrodia
10.29007/c5kn ◽  
2018 ◽  
Author(s):  
Alessio Bonadio ◽  
Francesco Chiti ◽  
Romano Fantacci

In this paper, the novel Fog Communications and Computing paradigm is addressed by presenting an integrated system architecture, that is applied to achieve a full con- text awareness for vehicular networks and, consequently, to react on traffic anomalous conditions. In particular, we propose to adopt a specific co-designed approach involving Application and Networks Layers. For the latter one, as no infrastructure usually exists, effective routing protocols are needed to guarantee a certain level of reliability of the in- formation collected from individual vehicles. As a consequence, we investigated classical Epidemic Flooding based, Network Coding inspired and Chord protocols. Besides, we resort to Blockchain principle to design a distributed consensus sensing application. The system has been tested by resorting to OMNeT++ framework for its modularity, high fidelity and flexibility. Performance analysis has been conducted over realistic scenarios in terms of consensus making overhead, latency and scalability, pointing out the better trade-off allowing the overlay P2P network formation and the complete context awareness achieved by the vehicles community.


2018 ◽  
Vol 17 (3) ◽  
pp. 155-160 ◽  
Author(s):  
Daniel Dürr ◽  
Ute-Christine Klehe

Abstract. Faking has been a concern in selection research for many years. Many studies have examined faking in questionnaires while far less is known about faking in selection exercises with higher fidelity. This study applies the theory of planned behavior (TPB; Ajzen, 1991 ) to low- (interviews) and high-fidelity (role play, group discussion) exercises, testing whether the TPB predicts reported faking behavior. Data from a mock selection procedure suggests that candidates do report to fake in low- and high-fidelity exercises. Additionally, the TPB showed good predictive validity for faking in a low-fidelity exercise, yet not for faking in high-fidelity exercises.


2019 ◽  
Vol 12 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Horea Pauna ◽  
Pierre-Majorique Léger ◽  
Sylvain Sénécal ◽  
Marc Fredette ◽  
Élise Labonté-Lemoyne ◽  
...  

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