Better health is essential to human happiness and well-being. It also makes an important contribution to economic progress, as healthy populations live longer, are more productive, and save more. Access pharmacy and drug resources are central in a successful healthcare system. That is why, it is important to localize and know pharmacies close to patient when he need a specific drug with availability and prices of this one through a mobile application. Mobile applications are accelerating day by day because more and more people using mobile phones, smartphones and tablets. We propose in this paper CamPharma, a mobile application that allows: (1) search pharmacies owning specific drug, (2) view the price of it, (3) viewing detailed information for a pharmacy, (4) viewing drug's details and cons-indications, (5) configure and receive alerts about taking drugs and (6) find guard pharmacies. It is a clientserver application and compatible with Android and Unstructured Supplementary Service Data (USSD). For Android users, the client is installed on the Android phone and the server part is installed on the CamPharma server. USSD users use Short Message Service (SMS) or call. The search function in CamPharma is a mathematical optimization problem expressed by an objective function f which determines five better pharmacies among N. Campharma is adapted to the context of pharmacies in Cameroon and we realized a prototype on Emerginov platform.

The traffic classification problem formulation is NP-hard and has known several resolution approaches where the emerging one is the machine learning approach. However, these approaches have primarily focused on traditional wired and wireless networks and rarely on Software-Defined Wireless Mesh Networks (SD-WMNs). A Software-Defined Network (SDN) makes network monitoring easier by separating the control plane of the network from the data plane. This paper discusses the limits of traffic classification in the network and proposes an approach based on supervised ensemble machine learning adapted to SD-WMN to classifier traffic efficiently in three stages: (a) a traffic-monitoring phase, (b) an IP flow collection phase and, (c) a traffic classification phase by the ensemble supervised machine learning. Ensemble methods are techniques that aim at improving the accuracy of results in models by combining multiple models instead of using a single model. The combined models significantly increase the accuracy of results. We performed experiments on Mininet-wifi emulation platform as data plane with Ryu as SDN controller in control plane. The supervised ensemble learning yields: (a) for the Bagging algorithm with the Random Forest algorithm, an accuracy of 99.90%, with an F1 score of 99.90% and, (b) for Boosting with the XGBoost algorithm, an accuracy of 99.97% with an F1 score of 99.96%. XGBoost appears as the best traffic classification model.

The exploitation of Wireless Sensor Networks (WSN) is constrained by limited power, low computing power and storage and short-range radio transmission. Many routing protocols respecting these constraints were developed but, it still lacks formal and standardized solutions being able to help in their configuration. The configuration management that responds to this concern is very important in this type of network. It consists of the definition of data models to configure and is very necessary for the good network performance. Tangible results were obtained in traditional networks with the emergence of NETCONF and YANG standards, but on the best of our humble knowledge there are none yet in WSNs. We propose in this paper wsn-routing-protocol, a YANG data model for routing protocols configuration in WSNs. Following our model, we propose two YANG configuration data models based on the latter: they are respectively aodv for AODV and rpl for RPL