A few years ago, the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) was proposed by IETF as the routing standard designed for classes of networks in which both nodes and their interconnects are constrained. Since then, great attention has been paid by the scientific and industrial communities for the protocol evaluation and improvement. Indeed, depending on applications scenarios, constraints related to the target environments or other requirements, many adaptations and improvements can be made. So, since the initial release of the standard, several implementations were proposed, some targeting specific optimization goals whereas others would optimize several criteria while building the routing topology. They include, but are not limited to, extending the network lifetime, maximizing throughput at the sink node, avoiding the less secured nodes, considering nodes or sink mobility. Sometimes, to consider the Quality of Service (QoS), it is necessary to consider several of those criteria at the same time. This paper reviews recent works on RPL and highlights major contributions to its improvement, especially those related to topology optimization, security and mobility. We aim to provide an insight into relevant efforts around the protocol, draw some lessons and give useful guidelines for future developments.

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.

ABSTRACT

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.

In this paper, we propose a cheap means for propagating mobile application using Bluetooth, a convenient short range wireless technology. Today, one of the main problems about mobile technology is about implementing automatically the software in the multiple and various type of phones devices, irrespective of the phone model (Nokia, Samsung, iPhone, etc.). There are many ways of doing OTA like by SMS. This approach allows providing an http download link by SMS but, we are limited by the maximum size one can have to build an application and send it trough this means. This approach is also expensive because data sources are expensive. Within the context of mobile social networking and proximity marketing, we come up with an original way of provisioning mobile applications using Bluetooth. We use anybody who is already a subscriber of an application to become an ambassador of it. The application is self-replicating and distributing in itself and sending back results. It ethically acts like a virus or a disease. A prototype is built to validate the proposed methodology