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 convergence of the Internet of Things (IoT) with cloud, edge, and fog computing has catalyzed a transformative shift in data processing and real‐time analytics across multiple sectors. This review explores the emerging IoT and cloud continuum, emphasizing its role in enhancing system scalability, reducing latency, and enabling intelligent, distributed decision‐making. A central contribution of this work is the design and implementation of a real‐time greenhouse monitoring and control system based on an FPGA platform, specifically the DE2‐115 development board with a Cyclone IV EP4CE115F29C7 device. The system integrates real‐time environmental sensing based on temperature, humidity, soil moisture, and light status with dynamic control of actuators such as fans, water pumps, LEDs, and humidifiers. Operating in both manual and autonomous modes, the system demonstrates high efficiency, utilizing only 2% of logic elements and < 3% of logic array blocks, while providing robust data visualization via onboard displays and a mobile interface. This practical implementation is contextualized within the broader IoT‐cloud continuum, illustrating how edge processing with FPGA complements cloud‐based analytics for precision agriculture. Moreover, the review investigates the architectures and protocols that underpin the continuum, addressing challenges such as security, privacy, and interoperability. Special attention is given to the role of AI, machine learning, and predictive analytics in enhancing decision‐making. The paper concludes with future research directions, highlighting the potential of emerging technologies including 5G, edge AI, and blockchain to overcome current limitations, enhance data processing, and drive innovation. By integrating real‐world application with theoretical advancement, this work offers a scalable, efficient, and intelligent model for future smart systems in agriculture, healthcare, smart cities, and beyond.

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.