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In the last recent years, the number of wireless devices has been growing and the security challenges increases too. Mobile Ad hoc Network (MANET) considers as a part of wireless network that connects mobile devices by using wireless channels without infrastructure. MANET use specific protocols to ensure the connectivity and exchange data between the source and destination. Optimized Link State Routing Protocol (OLSR) is a table-driven protocol that keep the route to all destination at any times, unfortunately it can be affected by many active routing attacks that reduce its performance by dropping the exchange packets or stopping the forward of data. In this paper we present a new approach to detect any active routing attacks by using the concept of Shapiro-Wilk test. Our method of detection is easy to implement and does not require any modification in the standard version of OLSR routing protocol as we will demonstrate by NS-3 simulations the detection of Black hole, Worm hole and Node isolation attacks that consider as most known attacks in MANET. A real experience is done by creating a small ad hoc network that connect six wireless devices by using OLSR protocol and finally we detect the presence of an active routing attack by applying our proposed method.

Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible.

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