Vehicular Delay-Tolerant Network (VDTN) is a special case of Delay-Tolerant Network (DTN) in which connectivity is provided by movement of vehicles with traffic prioritization to meet the requirements of different applications. Due to high node mobility, short contact time, intermittent connectivity, VDTNs use multi-copy routing protocols to increase message delivery rates and reduce the delay. However due to limited resources (bandwidth and storage capacity), these protocols cause the rapid buffer overflow and therefore the degradation of overall network performance. In this paper, we propose a buffer drop policy based on message weight by including traffic prioritization to improve the high priority messages delivery delay. Thus, the memory is subdivided into a high-weight queue and a low-weight queue. When the buffer is overflowing, and a new message arrives, the algorithm determines the message to be dropped in the queues considering that the current node is the destination of the message, the position of the current node with respect to the destination of the message and the age of the messages in the network.

No abstract

No abstract

Abstract-The purpose of this paper is to provide a decision support tool based on a mathematical model and an algorithm that can help in the assessment of the level of vulnerability of children in Côte d'Ivoire. So, this study was conducted in three phases, the first one includes the settlement of a data warehouse. Then the second involves the application of probabilistic model. The final phase deals with the classification of children considered vulnerable in descending order from the most to the least vulnerable. The purpose of this classification is to better manage the resources of donors to support vulnerable children. This work is part of the activities of UMRI The resilience of Côte d'Ivoire. This is to propose mathematical and computational tools to facilitate the work of the Centre for social resilience. The use of the context of children made vulnerable due to crises or diseases is an example of practical application of our social resilience model