Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVM is used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest for the same application.

Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem in the context of using CAN bus for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, this paper aims to concentrate on the application of data analysis and statistical learning techniques. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques under consideration in this paper outperform other techniques that have been used previously.

Vehicular ad hoc network (VANET) is a set of vehicles trying to exchange security and comfort data even if they are not directly within radio range of each other. As VANET presented several challenges, the various contributions defined are not efficient to provide reliable services. Consequently, several researches and projects have been launched to develop an intelligent transportation system that guarantees both safety and comfort for users. Although the use of metaheuristics seems to be the most convenient to overcome these issues, actually, to the best of our knowledge, there are no studies addressing the uses of those approaches in VANET. This paper surveys and discusses different metaheuristics applied to solve the routing problem within VANET. Furthermore, technical challenges and future trends are treated and presented.