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Automatic ElectroEncephalogram EEG classification for Stress detection represents a crucial interest, simultaneously with the increasing deaths caused by depression and psychological effects. Accurate automatic classification of EEG signals represents a complex task, requiring the use of sophisticated algorithms. In this light, we focus through this work on achieving the automatic stress detection from EEG signals, to help clinicians to get the true diagnosis in an early stage. At this light, we opt through this paper to the implementation of a proposed Recurrent Neural Network RNN model for automatic stress detection. The proposed work employs a pre-processing combined with Recurrent Neural Network models such as Gated Recurrent Unit (GRU). We have applied the FFT transformation on EEG signals, available from Kaggle. The EEG classification results have reached 97.23% for train, 93.68% for validation and 88.86% for the test process, by implementing GRU based SGD optimizer networks. To get more accurate results, Adam optimizer has been implemented, achieving results equal to 99.53%, for the train, 94.98% for the validation and 89% for the test process. Moreover, stress emotions have been well detected as demonstrated by the confusion matrix results. Finally, accuracy and loss curves show promising results for both training and validation and the error rate is too close to zero. Our proposed RNN model, with its reduced number of parameters shows to be an excellent application to be implemented on embedded systems, thanks to its lightweight reducing both training time and memory consumption. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has witnessed a significant surge in adoption, particularly through the utilization of Wireless Sensor Networks (WSNs), which comprise small internet-connected devices. These deployments span various environments and offer a multitude of benefits. However, the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities. In response to this, the Internet Engineering Task Force (IETF) has developed the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) to address the unique requirements of such networks. Recognizing the critical role of RPL in maintaining high performance, this paper proposes a novel approach to optimizing power consumption. Specifically, it introduces a developed sensor motes topology integrated with a Radio Duty Cycling (RDC) mechanism aimed at minimizing power usage. Through rigorous analysis, the paper evaluates the power efficiency of this approach through several simulations conducted across different network topologies, including random, linear, tree, and elliptical topologies. Additionally, three distinct RDC mechanisms--CXMAC, ContikiMAC, and NullRDC--are investigated to assess their impact on power consumption. The findings of the study, based on a comprehensive and deep analysis of the simulated results, highlight the efficiency of ContikiMAC in power conservation. This research contributes valuable insights into enhancing the energy efficiency of RPL-based IoT networks, ultimately facilitating their widespread deployment and usability in diverse environments. [ABSTRACT FROM AUTHOR]

The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem.Despite its significance, RPL's susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol's vulnerability to the decreased rank attack in both static and mobile network environments.We employ the RandomDirectionMobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). Our findings illuminate the disruptive impact of this attack on the routing hierarchy, resulting in decreased PDR and throughput, increased AE2ED, ETX, and APC. These results underscore the urgent need for robust security measures to protect RPLbased IoT networks. Furthermore, our study emphasizes the exacerbated impact of the attack in mobile scenarios, highlighting the evolving security requirements of IoT networks. [ABSTRACT FROM AUTHOR]