To monitor environments, Wireless Sensor Networks (WSNs) are used for collecting data in divers domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, we adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. Our approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. Our analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, we use SHAP method to explain predictions.

This study is related to the detection of fake accounts on Instagram dataset that used by previous works. For this purpose, various Machine Learning algorithms have been used such as Bagging and Boosting to detect fake accounts on Instagram. Machine Learning now allows eight to learn directly from data rather than human knowledge, with an increased level of accuracy. To balance the two classes of data, we used the SMOTE algorithm which allows to obtain the same number of individuals for each class. We also incorporated methods for interpreting complex Machine Learning Models to understand the reasons for a model decision like SHAP values and LIME, we preferred to use SHAP values because it provides a local and global explanation of the model and also the values add up to the real estimation of the model, which LIME does not provide. Results show an overall accuracy of 96% for the XGBoost and Random Forest. In what follows, an online fake detecting system has been developed to detect malicious accounts on the Instagram.

Message Ferries is a mechanism of data dissemination in delay tolerant networks (DTN) where one or more nodes are tasked with storing and carrying data between source and destination nodes. DTN uses message ferries in order to ensure connectivity between all nodes. However, the movement of ferries in the network consumes a certain amount of energy, which leads to network performance degradation. Thus, network resources allocation is of importance. The goal of this work is to find an efficient number of message ferries to be involved in the network.

In this work we deal with the topology adapted to routing in delays tolerant networks (DTN). This topology plays a very important role in the design and implementation of routing protocols in this network deprived of any infrastructure and centralized administration with intermittent connectivity. In fact, we propose a DTN routing hierarchical topology (DRHT) that incorporates three basic concepts: Message Ferries, ferrie routes and clusters. This approach forms a structure capable to adapt dynamically to changes in the mobile environment. We provide also an asynchronous algorithm that will minimize the number of messages replicated and the network resources in general. Finally, we will present the simulation results demonstrating the effectiveness of the topology and routing protocol, particularly for high density network nodes.