The rise of autonomous vehicles necessitates advanced communication networks for effective data exchange. The routing protocols Ad hoc On-Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR) are vital in mobile networks (MANETs) and vehicular ad hoc networks (VANETs). However, their performance is affected by changing network conditions. This study examines key routing parameters—MaxJitter, Hello/Beacon Interval, and route validity time—and their impact on AODV and GPSR performance in urban and highway scenarios. The simulation results reveal that increasing MaxJitter enhances AODV throughput by 12% in cities but decreases it by 8% on highways, while GPSR throughput declines by 15% in cities and 10% on highways. Longer Hello intervals improve AODV performance by 10% in urban settings but reduce it by 6% on highways. Extending route validity time increases GPSR’s Packet Delivery Ratio (PDR) by 10% in cities, underscoring the need to optimize routing parameters for enhanced VANET performance.

Vehicular Ad Hoc Networks play a crucial role in enabling Smart City applications by facilitating seamless communication between vehicles and infrastructure. This study evaluates the throughput performance of different routing protocols, specifically AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM, under various city and highway scenarios in complex networks. The analysis covers key parameters including traffic generation, packet sizes, mobility speeds, and pause times. Results indicate that TOM and DEM profiles significantly improve throughput compared to traditional AODV and GPSR protocols. GPSR:TOM achieves the highest throughput across most scenarios, making it a promising solution for high-performance data transmission in Smart Cities. For instance, GPSR:TOM achieves an average throughput of 3.2 Mbps in city scenarios compared to 2.8 Mbps for GPSR, while in highway scenarios, the throughput increases to 3.6 Mbps. Additionally, AODV:DEM records a throughput of 3.4 Mbps for high traffic generation, outperforming AODV:TOM at 3.1 Mbps and baseline AODV at 2.7 Mbps. The findings highlight the importance of optimizing data throughput to ensure reliability and efficiency in complex vehicle interconnection systems, which are critical for traffic management, accident prevention, and real-time communication in smart urban environments.

The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance AD staging using structural MRI. The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. The proposed technique, referred to as Red-KPLS-CNN, refines MRI features into discriminative biomarkers while minimizing redundancy. As a result, the framework achieves 96.9% accuracy and an F1-score of 97.8% in the multiclass classification of AD cases using the Kaggle dataset. The dataset was strategically partitioned into 60% training, 20% validation, and 20% testing sets, preserving class balance throughout all splits. The integration of Red–KPLS enhances feature selection, reducing dimensionality without compromising diagnostic sensitivity. Compared to conventional models, our approach improves classification robustness and generalization, reinforcing its potential for scalable and interpretable AD diagnostics. These findings emphasize the importance of hybrid wavelet–kernel–deep learning architectures, offering a promising direction for advancing computer-aided diagnosis (CAD) in clinical applications.

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems.