With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management.

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works.

Most studies indicate that bimodal emotion recognition systems are more objective and accurate. However, many of these systems depend on physiological signals that require direct measurement, which introduces certain limitations. This study aims to develop a new bimodal emotion recognition system based on skin temperature (SKT) and heart rate variability (HRV) using hybrid neural networks. Notably, these physiological signals can be measured remotely, addressing the limitations of direct measurement methods. The integration of these modalities enables the model to effectively utilize both spatial and temporal features for robust emotion classification. The hybrid neural network, combining a convolutional neural network and a gated recurrent unit (CNN+GRU), was trained on experimental SKT and HRV data collected from individuals experiencing basic emotions such as anger, disgust, fear, happiness, sadness, and surprise. The trained model achieved an accuracy of 95.58%, outperforming existing approaches that use only a single data modality. Confusion matrix analysis demonstrated high accuracy in recognizing most basic emotions. The results confirm the effectiveness of the proposed approach in combining physiological and visual signals for improved emotion recognition.

Optimal trade-off between energy efficiency and data transmission reliability serves as a solid foundation for improving the stability and performance of LoRaWAN networks in diverse Internet of Things (IoT) applications. This study investigates the modeling and optimization of energy consumption (EC) in wireless sensor networks (WSNs) using LoRa technology. Key transmission parameters – spreading factor (SF), bandwidth (BW), code rate (CR), and transmission power (TP) – along with the node-to-gateway distance were analyzed for their impact on network performance and energy efficiency. The simulations were conducted using the FLoRa simulation framework integrated with OMNeT++, and the XGBoost machine learning model was used to predict the optimal configurations. The results show that the proposed optimization approach achieves 28% reduction in energy consumption and 24% improvement in packet delivery ratio (PDR) compared to traditional adaptive data rate (ADR) algorithm.

Bifacial photovoltaic cells represent a promising solution for increasing the energy efficiency of solar modules by utilizing the rear surface. In this study, the bifacial solar cell was modeled based on the single-diode model. Current-voltage (I-V) and power-voltage (P-V) characteristics were constructed for various bifacial factor values, allowing us to consider both typical operating conditions and configurations with increased reflectivity of the rear surface. Calculations showed that a 36-cell module provides a 3.5% increase in efficiency compared to a monofacial module. The annual energy output of the bifacial module was 910 Wh versus 517 Wh for the monofacial module, representing a 76% increase. Daily and monthly energy production profiles confirmed the significant contribution of the rear side and the consideration of seasonal fluctuations in solar radiation. These results demonstrate the high potential of bifacial modules to improve power generation and overall energy efficiency of photovoltaic systems.