Mobility has always represented a complicated phenomenon in the network routing process. This complexity is mainly facilitated in the way that ensures reliable connections for efficient orientation of data. Many years ago, different studies were initiated basing on routing protocols dedicated to static environments in order to adapt them to the mobile environment. In the present work, we have a different vision of mobility that has many advantages due to its ’mobile’ principle. Indeed, instead of searching to prevent mobility and testing for example to immobilize momentarily a mobile environment to provide routing task, we will exploit this mobility to improve routing. Based on that, we carried out a set of works to achieve this objective. For our first contribution, we found that the best way to make use of this mobility is to follow a mobility model. Many models have been proposed in the literature and employed as a data source in most studies. After a careful study, we focused on the Random Waypoint mobility model (RWP) in order to ensure routing in wireless networks. Our contribution involves a Random Waypoint model (in its basic version) that was achieved on the TOSSIM simulator, and it was considered as a platform for our second (and main) contribution, in which we suggested an approach based RWP where network nodes can collaborate and work together basing on our recommended algorithm. Such an approach offers many advantages to ensure routing in a dynamic environment. Finally, our contributions comprise innovative ideas for suggesting other solutions that will improve them.

Despite the significant advances achieved these recent last years in terms of technologies widespread use in medical education, clinical reasoning learning (CRL) remains an extremely hard task in which there are still many gray areas that should be enlightened to better understand it. Furthermore, while CRL is basically a collaborative task implying the participation of many students and tutors working simultaneously on a same case, it should be considered from a social perspective. The authors followed then a collaborative-based learning approach, which consists in designing a shared workspace to support collaboration and enable social clinical knowledge acquisition. They started with a deep analysis of the CRL process in order to understand the usual way under which students learn together and then, highlight the vital collaborative learning tasks that need to be supported. The resulting designed model allowed us to shift towards Collaborative CRL (CCRL).

In collaborative clinical learning field, several recent pertinent studies showed that gathering learners with their tutor still insufficient to improve students' learning quality and knowledge acquisition. Consequently, focusing attention on professional skills within a collaboration environment seems to be the most appropriate way to reach the wished learning objectives, particularly in a complex specialty such as medical diagnosis learning. In this paper, we firstly introduce the concept of medical diagnosis from cognitive studies view that have been performed in the field of medical education. Then, we will discuss our shared web environment designed to support distance diagnosis learning, which aims to promote knowledge coconstruction and collaboration between learner

Cet article traite de la problématique du soutien à apporter à l’apprenant en situation de formation à distance asynchrone sur le web. Nous présentons à cet effet un système tuteur intelligent (STI) basé sur la technologie hypermédia, construit dans le cadre de la plate-forme d’enseignement à distance IBN SINA. Ce STI construit selon une architecture client-serveur, consiste en un canevas générique que les auteurs instancient à travers un mode auteur coopératif. Construit autour des technologies XML et Systèmes Experts, ce système permet une adaptation dynamique de l’activité d’apprentissage, et ce, en se basant sur trois modèles : un modèle pédagogique (la pédagogie par objectif), un modèle de métadonnées (le standard LOM) et un modèle de l’apprenant (de type overlay). Après avoir présenté la problématique de soutien à l’apprenant à distance, nous décrivons brièvement la modélisation de notre système ainsi que son architecture logicielle.