Development of Low Power IoT Nodes for Localization and Environmental Sensing

The rapid evolution of the Internet of Things (IoT) has led to the widespread adoption of smart, interconnected devices capable of collecting and transmitting real-time data. Advances in electronics and wireless communications have enabled the development of low-power sensor nodes that can operate autonomously for extended periods without frequent maintenance. These devices find applications in various fields, including environmental monitoring and indoor positioning systems. One of the major challenges in IoT node design is energy management, as many devices are deployed in remote or hard-to-access locations where battery replacement is complex and costly. Ensuring long operational autonomy requires optimizing hardware and software energy consumption, implementing advanced power management strategies, and integrating energy harvesting technologies such as solar cells. In the context of indoor positioning, the absence of a universally accepted standard technology presents a significant challenge. While GPS is the dominant solution for outdoor localization, its effectiveness is severely limited in indoor environments due to satellite signal attenuation. This limitation has led to the development of alternative positioning systems based on radio waves, ultrasound, magnetic fields, and Visible Light Communication (VLC). Among these, VLC stands out as an efficient and cost-effective solution by leveraging existing lighting infrastructures to provide both illumination and positioning data. Similarly, in environmental monitoring, IoT sensor networks enable real-time climate data collection, improving resource management in applications such as precision agriculture. Monitoring key atmospheric parameters such as temperature, humidity, leaf wetness, and wind allows for better crop protection, optimized irrigation, and effective disease prevention. This thesis focuses on developing and optimizing low-power IoT nodes for indoor positioning and environmental monitoring by integrating advanced energy management strategies and innovative technologies to enhance data accuracy. The research investigates indoor positioning technologies, with a particular emphasis on VLC-based localization and energy-autonomous sensor nodes, and explores the design and analysis of indoor solar cells for energy harvesting and battery management. The development of a VLC-based indoor positioning system is examined in detail, covering hardware, software, and experimental validation, while neural network-based localization techniques are implemented to improve accuracy in 2D and 3D positioning. Additionally, the study analyzes IoT-based environmental monitoring systems applied to precision agriculture, focusing on key climatic parameters that influence plant health, and evaluates the performance and energy efficiency of the developed monitoring system through experimental testing. The experimental results demonstrate the effectiveness of the proposed solutions in terms of accuracy and energy autonomy. The VLC-based indoor positioning system achieved a maximum error of 3.97 cm and an average error of 1.28 cm, well below the acceptable 5 cm threshold. The system also proved resilient to external light interference, ensuring reliable performance in real-world scenarios. Moreover, the integration of solar energy harvesting allowed autonomous operation without the need for battery replacements, significantly enhancing sustainability. The research further explores the application of neural networks in localization, showing that a multiple-network approach improves positioning accuracy, reducing the average localization error to 1 cm. The system also demonstrated robustness against obstacles, confirming its suitability for practical indoor environments. Regarding environmental monitoring, the developed IoT-based weather station successfully collected reliable data on key climate parameters, providing valuable insights for precision agriculture. The findings indicate that adjusting transmission intervals can significantly extend battery life, with low-power strategies enabling operational autonomy for up to a year. The integration of solar panels could further enhance system sustainability, enabling long-term, maintenance-free monitoring. This study highlights how the combination of low-power IoT nodes, VLC-based localization, and environmental monitoring can lead to reliable, scalable, and energy-efficient solutions. The integration of artificial intelligence and energy harvesting technologies represents a promising approach to optimizing performance and reducing maintenance needs. Future developments may include further improvements in localization algorithms, expansion of sensor networks, and AI-driven predictive analytics to enhance the precision and efficiency of IoT applications.