This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.

The Internet of Things (IoT) has transformed the way devices interact, enabling applications in domains such as smart cities, industrial automation, agriculture, and healthcare. LoRaWAN (Long Range Wide Area Network) has emerged as a leading Low Power Wide Area Network (LPWAN) protocol, offering long-range communication capabilities, energy efficiency, and support for large-scale IoT deployments. This review article provides a comprehensive analysis of the LoRaWAN protocol, detailing its architecture, technical features, and its role in addressing IoT connectivity challenges. Despite its advantages, LoRaWAN faces significant challenges, including scalability issues, interference in unlicensed frequency bands, energy constraints, and security vulnerabilities. Recent innovations such as adaptive channel management, dynamic power optimization, blockchain-based security enhancements, and advanced scheduling algorithms are explored as solutions to these challenges. The paper also highlights key future research directions, including the integration of artificial intelligence for intelligent resource management, hybrid network approaches for enhanced reliability, and advancements in energy-efficient communication mechanisms. By addressing these areas, LoRaWAN can continue to evolve as a cornerstone technology for future IoT ecosystems.