Remote Monitoring

This paper presents implementation of cost-effective solar powered hydroponic system with Internet of Things (IoT) for online and remote checking and controlling the system operation. The objective was to take advantage of the abundant solar energy while incorporating IoT platform into the system. The system is made up of power supply unit, control unit, Wi-Fi unit, input unit and output unit. The system monitored temperature, humidity and volume of the nutrient solution and at the same time sent online messages to the farmer. The test carried out showed that the system is working and the readings obtained online showed a ± 1% variation from the physical measurement done remotely with meters. This implies that the online control of the system operation was effective

Background: The growing complexity of telecommunications networks, fueled by advancements like the Internet of Things (IoT) and 5G, necessitates dynamic and real-time network performance monitoring. Traditional static systems often fail to address challenges related to scalability, adaptability, and response speed in high-demand environments. Integrating artificial intelligence (AI) with unmanned aerial vehicles (UAVs) presents a transformative approach to overcoming these limitations. Objective: This study aims to evaluate the effectiveness of AI-driven drones for real-time network performance monitoring, focusing on key metrics such as latency, signal strength, throughput, and anomaly detection. Methods: A comprehensive framework was developed, employing reinforcement learning (RL) for path planning and a hybrid temporal-spectral anomaly detection (HTS-AD) algorithm. Experimental validation was conducted using 10 UAVs across simulated and real-world environments, collecting over 3.2 million data points. Statistical analyses, including MANOVA and Bayesian regression, were used to evaluate performance. Results: The proposed system demonstrated significant improvements over traditional methods, including a 24.6% increase in anomaly detection accuracy, a 30% reduction in energy consumption, and 99.9% network coverage in high-density UAV deployments. Conclusion: AI-driven drones offer a scalable, efficient, and reliable solution for network monitoring. By addressing limitations of traditional systems, this study establishes a foundation for next-generation telecommunications infrastructure. Future research should focus on real-world deployment and hybrid security models.