Real-time monitoring, data-driven decisions, and energy consumption optimization have reached a new level with IoT advancement. However, a significant challenge faced by intelligent nodes and IoT applications resides in their energy requirements in the long term, especially in the case of gas or water smart meters. This article proposes an algorithm for smart meters’ energy consumption optimization based on IoT, LoRaWAN, and NB-IoT using microcontroller-based development boards, PZEM004T energy meters, Dragino LoRaWAN shield, or BG96 NB-IoT modules. The algorithm adjusts the transmission time based on the change in data in real-time. According to the experimental results, the energy consumption and the number of packets transmitted significantly decreased using LoRaWAN or NB-IoT, saving up to 76.11% and 86.81% of the transmitted packets, respectively. Additionally, the outcome highlights a notable percentage reduction in the energy consumption spike frequency, with NB-IoT achieving an 87.3% reduction and LoRaWAN slightly higher at 88.5%. This study shows that adaptive algorithms are very effective in extending the lifetime of IoT nodes, thereby providing a solid baseline for scalable, lightweight, energy-monitoring IoT applications. The results could help shape the development of smart energy metering systems and sustainable IoT.

Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficiencies of EfficientNetB0 with the deep representational capabilities of ResNet50. The model is designed to detect early signs of driver fatigue through advanced video-based analytics by leveraging both computational scalability and deep feature learning. Extensive experiments were conducted on three benchmark datasets—SUST-DDD, YawDD, and NTHU-DDD—to validate the model’s performance across a range of environmental and demographic variations. EffRes-DrowsyNet achieved 97.71% accuracy, 98.07% precision, and 97.33% recall on the SUST-DDD dataset. On the YawDD dataset, it sustained a high accuracy of 92.73%, while on the NTHU-DDD dataset, it reached 95.14% accuracy, 94.09% precision, and 95.39% recall. These results affirm the model’s superior generalization and classification performance in both controlled and real-world-like settings. The findings underscore the effectiveness of hybrid deep learning models in real-time, safety-critical applications, particularly for automotive driver monitoring systems. Furthermore, EffRes-DrowsyNet’s architecture provides a scalable and adaptable solution that could extend to other attention-critical domains such as industrial machinery operation, aviation, and public safety systems.

With rapid advancements in the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) play a crucial role in expanding IoT’s capabilities while using minimal energy. Among the various LPWAN technologies, LoRaWAN (Long-Range Wide-Area Network) is particularly notable for its capacity to enable long-range, low-rate communications with low power needs. This study investigates how to optimize the placement of LoRaWAN gateways by using a combination of Particle Swarm Optimization (PSO) and Differential Evolution (DE). The approach is validated through simulations driven by HTZ to evaluate network performance in urban settings. Centered around the area of the Politehnica University of Bucharest, this research examines how different gateway placements on various floors of a building affect network coverage and packet loss. The experiment employs Adeunis Field Test Devices (FTDs) and Dragino LG308-EC25 gateways, systematically testing two spreading factors, SF7 and SF12, to assess their effectiveness in terms of signal quality and reliability. An innovative optimization algorithm, GateOpt PSODE, is introduced, which combines PSO and DE to optimize gateway placements based on real-time network performance metrics, like the Received Signal Strength Indicator (RSSI), the Signal-to-Noise Ratio (SNR), and packet loss. The findings reveal that strategically positioning gateways, especially on higher floors, significantly improves communication reliability and network efficiency, providing a solid framework for deploying LoRaWAN networks in intricate urban environments.

Recently, driver inattention has become the leading cause of automobile accidents. As a result, the driver's perception and decision-making abilities are diminished, and the driver can lose control of the car. To prevent accidents caused by driver inattention, it’s vital to continuously monitor the driver and his driving behaviour and inform him if he becomes distracted or sleepy. This topic has been the subject of study for decades. Whenever feasible to recognise unsafe driving in advance, accidents could be avoided. This document presents an overview of the existing driver alertness system and the various techniques for detecting driver attentiveness.

Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less suitable for real-time applications due to their high computational complexity, risk of overfitting, and reliance on large datasets. Hence, this paper introduces an innovative approach that integrates fast neighbourhood component analysis (FNCA) with a deep neural network (DNN) to enhance the detection of driver drowsiness using electroencephalogram (EEG) data. FNCA is employed to optimize feature representation, effectively highlighting critical features for drowsiness detection, which are then analysed using a DNN to achieve high accuracy in recognizing signs of driver fatigue. Our model has been evaluated on the SEED-VIG dataset and achieves state-of-the-art accuracy: 94.29% when trained on 12 subjects and 90.386% with 21 subjects, surpassing existing methods such as TSception, ConvNeXt LMDA-Net, and CNN + LSTM.