The use of Micro-Electro-Mechanical Systems (MEMS)-based Inertial Measurement Units (IMUs) is widespread in many applications concerning monitoring, diagnostic, and/or controlling in navigation and transportation systems, as well as in low-cost applications for automotive and aeronautical fields. The data provided by the set of sensors typically present in IMUs, as accelerometers, gyroscopes, and magnetometers, are often used also for feeding suitable filtering and positioning algorithms able to correct the attitude and path of the vehicle on which they are installed or to provide the analytical redundancy needed for online diagnosis. Nevertheless, from one hand, the performance of low-cost MEMS-based IMUs are certified only under a small set of nominal operating conditions, and on the other hand, the filtering algorithms are often designed and verified under canonical additive noises. In this framework, the paper proposes a test-plan and a test setup for analyzing and characterizing the performance of filtering algorithms for positioning based on data coming from low-cost IMUs, and able to verify systematically the operating of such algorithms under real scenarios. Two kinds of very popular filtering algorithms have been considered, namely the Complementary filter and the Attitude and Heading Reference Systems (AHRS) Kalman filter, which belongs to two opposite approaches. The experimental results prove how the typical vibrations present in real scenarios can significantly affect the performance of such algorithms.

This paper describes the implementation and experimental verification of an instrument fault detection (IFD) scheme for stroke sensors. In the scheme, a nonlinear autoregressive with exogenous inputs (NARX) neural network works as a soft sensor for the generation of residuals, while a rule-based decision maker provides the fault detection and classification. The scheme was thought to be implemented in the firmware of central units for the control of semiactive suspension systems for motorbikes. Execution times compatible with the real-time application constraints are reached through straightforward programing rules and suitable code optimization. These times were obtained with machine parameter values (such as clock frequency and absorbed current) far below the upper limit of the range, thanks to the adoption of a programing methodology specially designed for the real-time implementation of diagnostic schemes on general-purpose microcontrollers (MCUs). The diagnostic performance of the scheme was verified through an experimental campaign, carried out on a motorcycle featured with electronic controlled semiactive suspensions. Promptness and reliability in detecting the most common faults of the rear stroke sensor resulted fully compatible with actual applications’ expected values.

Face-based recognition systems have been increasingly used in many different applications in today's society, starting from surveillance and access control to the authentication for banking activities. Therefore, in the last few years an increasing interest in the performance characterization and improvement of such systems can be found in the scientific community. Most of the methodologies for testing the performance of such systems are based on the evaluation of recognition reliability indexes that are generally related to the probability of a false positive and/or of a false negative.

Cyber security is one of the most crucial aspects of the Internet of Things (IoT). Among the possible threats, great interest is today paid toward the possible capturing of information caused by external attacks on both client and server sides. Whatever the IoT application, the involved nodes are exposed to cyberattacks mainly through the vulnerability of either the sensor nodes themselves (if they have the capabilities for networking operativity) or the IoT gateways, which are the devices able to create the link between the local nodes of the IoT network, and the wide area networks. Due to the low-cost constraints typical of many IoT applications, the IoT sensor nodes and IoT gateways are often developed on low-performance processing units, in many cases customized for the specific application, and thus not easy to update against new cyber threats that are continuously identified. In the framework of cyberattacks aimed at capturing sensitive information, one of the most known was the heartbleed, which, has allowed attackers to remotely read protected memory from an estimated 24–55 percent of popular HTTPS sites. To overcome such a problem, which was due to a bug of the OpenSSL, a suitable patch was quickly released, thus allowing to avoid the problem in most of the cases. However, IoT devices may require more advanced mitigation techniques, because they are sometimes unable to be patched for several practical reasons. In this scenario, the paper proposes a novel measurement method for inline detecting intrusions due to heartbleed and heartbleed-like attacks. The proposed solution is based on an effective rule which does not require decoding the payload and that can be implemented on a low-performance general-purpose processing unit. Therefore, it can be straightforwardly implemented and included in either IoT sensor nodes or IoT gateways. The realized system has been tested and validated on a number of experiments carried out on a real network, showing performance comparable (in some cases better) with the heavier machine learning-based methods.