University of Avignon

About: University of Avignon is a education organization based out in Avignon, Provence-Alpes-Côte-d'Azur, France. It is known for research contribution in the topics: Population & Speaker recognition. The organization has 1526 authors who have published 3766 publications receiving 88928 citations.

Scale- and taxon-dependent patterns of plant diversity in steppes of Khakassia, South Siberia (Russia)

Abstract: The drivers of plant richness at fine spatial scales in steppe ecosystems are still not sufficiently understood. Our main research questions were: (i) How rich in plant species are the natural steppes of Southern Siberia compared to natural and semi-natural grasslands in other regions of the Palaearctic? (ii) What are the main environmental drivers of the diversity patterns in these steppes? (iii) What are the diversity–environment relationships and do they vary between spatial scales and among different taxonomic groups? We sampled the steppe vegetation (vascular plants, bryophytes and lichens) in Khakassia (Russia) with 39 nested-plot series (0.0001–100-m2 plot size) and 54 additional 10-m2 quadrats across the regional range of steppe types and measured various environmental variables. We measured β-diversity using z-values of power-law species–area relationships. GLM analyses were performed to assess the importance of environmental variables as predictors of species richness and z-value. Khakassian steppes showed both high α- and β-diversity. We found significant scale dependence for the z-values, which had their highest values at small spatial scales and then decreased exponentially. Total species richness was controlled predominantly by heat load index, mean annual precipitation, humus content and soil skeleton content. The positive role of soil pH was evident only for vascular plant species richness. Similar to other studies, we found that the importance of environmental factors strongly differed among taxonomic groups and across spatial scales, thus highlighting the need to study more than one taxon and more than one plot size to get a reliable picture.

Jamming Attack Model and Detection Method for Beacons Under Multichannel Operation in Vehicular Networks

Abstract: Safety applications in vehicular networks involve real-time information included in periodical exchanged messages, called beacons. Denial of Service (DoS) attacks, especially jamming, may have high impact on these basic safety messages mainly because of the adequate condition of the attack at the lower layers. Due to the important role of these safety messages, securing beacons against jamming attacks is primordial. First, we investigate the impact of jamming attacks on broadcasting by introducing a new analytical model. We show that the degradation at certain levels in network performance is an indication of a jamming attack in the network; therefore, results from this analysis will allow us to determine network performance thresholds to distinguish normal operation and attacks. Second, we study the feasibility of the existing threshold-based methods to detect jamming in real-time applications. From our analysis, these methods are not suitable for safety applications. Hence, we propose a real-time Medium-Access-Control-based (MAC-based) detection method detection method to meet the requirements of safety applications in vehicular networks. Our detection method can more accurately distinguish the causes of failed transmissions, such as contention collisions, interferences, and jamming attacks. The jamming attacks are detected with low probability of false alarms. Finally, we evaluate our detection method with both analytical analysis and network simulations. Our detection method performs high detection probability in different studied scenarios while false alarms are almost avoided.