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The ultimate focus of this paper is to provide a hyperchaos-based reconfigurable platform for the real-time securing of communicating embedded systems interconnected in networks according to the Internet of Things (IoT) standards. The proposed platform’s Register Transfer Level (RTL) architecture is entirely developed and designed from scratch using the VHSIC Hardware Description Language (VHDL). The original idea consists of exploiting the nonlinearity of a discretized and optimized 4D Lorenz hyperchaotic system as an encryption keystream generator in a symmetric cryptosystem to secure wireless communicating embedded systems and adapted to the UDP/IP protocol. It was necessary to go through three essential steps to achieve this goal. First, a lightweight and energy-efficient hyperchaos-based encryption IP core is designed, implemented on an FPGA circuit and dedicated to IoT device security, denoted Hyperchaotic-based IoT Device Security Core (HC-IoT-DSC). The designed encryption IP core combines three subsystems: a multiple key size hyperchaotic key generator (HC-KG), a hyperchaotic synchronization by dynamic feedback modulation technique (HCS-DFM), and an online FIPS 140-2-based built-in self-security test (BISST) module. Second, a secure UDP/IP stack is totally implemented using the VHDL language. Third, the proposed architecture was integrated into real-world and real-time secure wireless communication at a distance of 2 km between two delocalized network nodes employing the Xilinx ML605 FPGA platform and the ZigBee E800-DTU module. A panoply of online/offline investigations and experiments were carried out intensely, deeply, and thoroughly to analyze, evaluate and validate the robustness and security aspects of the proposed scheme regarding all the aspects related to embedded system security. Notably, the evaluations were conducted in two phases for all the platform components before and after integrating the proposed security core in real-time wireless communication. The investigations and implementation findings validate that the proposed architecture can attain good performances, and confirm the feasibility of the adopted approach for IoT applications. Furthermore, the timing and power efficiency results present an excellent trade-off between design performance and high-security achievement.

This study was conducted for six cities in southern Algeria, where the accuracy of three models--support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)--were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors--correlation coefficient, relative root mean squared error and mean absolute percent error--with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H0), declination and average temperature (Tavg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible. [ABSTRACT FROM AUTHOR]

This article presents a Johansen test assessing the predetermined long-term relationships of datasets from a photovoltaic (PV) plant to predict the power output of an island zone. The goal was to use Johansen's model to predict the PV power generation in the island of Mauritius. In this article, time series using an on-site measurement dataset have been used to design an original prediction model, the Johansen model for PV power output. This model is trained to predict random monthly, weekly, and daily PV power outputs in different seasons and years. The experimental results demonstrate that the Johansen model is a powerful medium-term predicting tool. [ABSTRACT FROM AUTHOR]

This paper presents a design and implementation proposal for a real-time frequency measurement system for high-precision, multi-channel quartz crystal microbalance (QCM) sensors using a field programmable gate array (FPGA). The key contribution of this work lies in the integration of a frequency measurement and mass resolution computation based on Global Positioning System (GPS) signals within a single FPGA chip, utilizing Input/Output Blocks to incorporate logic QCM oscillator circuits. The FPGA design enables parallel processing, ensuring accurate measurements, faster calculations, and reduced hardware complexity by minimizing the need for external components. As a result, a cost-effective and accurate multi-channel sensor system is developed, serving as a reconfigurable standalone measurement platform with communication capabilities. The system is implemented and tested using the FPGA Xilinx Virtex-6, along with multiple QCM sensors. The implementation on a Xilinx XC6VLX240T FPGA achieves a maximum frequency of 324 MHz and consumes a dynamic power of 120 mW. Notably, the design utilizes a modest number of resources, requiring only 188 slices, 733 flip-flops, and 13 IOBs to perform a double-channel sensor microbalance. The proposed system meets the precision measurement requirements for QCM sensor applications, exhibiting low measurement error when monitoring QCM frequencies ranging from 1 to 50 MHz, with an accuracy of 0.2 ppm and less than 0.1 Hz. [ABSTRACT FROM AUTHOR]