The mobility of nodes in a wireless sensor network is a factor affecting the quality of service offered by this network. We think that the mobility of the nodes presents an opportunity where the nodes move in an appropriate manner. Therefore, the routing algorithms can benefit from this opportunity. Studying a model of mobility and adapt it to ensure an optimal routing in an agitated network is the purpose of our work. We are interested in applying a variant of the mobility model RWP (named routing-random waypoint 'R-RWP') on the whole network in order to maximise the coverage radius of the base station (which will be fixed in our study) and thus to optimise the data delivery end-to-end delay.

Within the majority of contexts, it is persons that are considered to be competent or incompetent. However, in many cases it is the performance of groups and teams that is most important. This implies a concept of collective competence that integrates the set of skills in a group. In addition, the collective competence construction process is also enriched through collaboration which implies exchanges, confrontations, negotiations and interpersonal interactions. This paper presents our CSCW-based approach supporting collective competence construction. As a case of study, the industrial maintenance workspace is fundamentally a collaborative context. Our contribution in this area led us first, to analyse the related task in order to highlight collaborative maintenance vital needs and design the appropriate required group awareness supports which will be used to support collective competence. Finally, the experimentation study identifies the highly effective group awareness tools.

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).