This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML’s potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.

This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models—Inception, Xception, VGG16, AlexNet, and YoloV8—against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model’s performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production.

Orthogonal frequency division multiplexing (OFDM) is currently the most used technique for the high-data-rate transmission. However, OFDM systems have an inherent drawback in that the transmitted signals suffer from a high peak-to-average power ratio (PAPR). Partial transmit sequence (PTS) is generally applied to reduce the PAPR of an OFDM signal in wireless communication systems. Here, the search complexity for optimal phase vector increases exponentially with the number of phase vectors, since it involves an extensive random search over all the combinations of the allowed phase vectors. In this paper, a swarm intelligence algorithm for phase optimization based on the Firefly algorithm (FF) is applied to search the optimal combination of phase vectors. The proposed algorithm provides a superior trade-off between the improved PAPR performance and computational complexity when compared to the PTS scheme for a large number of sub-blocks. Simulation results show that the Firefly-algorithm-based PTS (FF-PTS) algorithm is an efficient method to achieve superior PAPR characteristics for OFDM signals when compared to conventional algorithms with very few parameters to adjust.