Design and Evaluation of Low-Power IoT Sensor Nodes Using Energy Harvesting

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) applications necessitates the development of energy-efficient sensor nodes capable of sustaining operational longevity in diverse environments. However, conventional power sources such as batteries impose limitations due to finite lifespan, maintenance costs, and environmental concerns, which hinder large-scale deployment and scalability of IoT networks. This study aims to design, implement, and evaluate low-power IoT sensor nodes integrated with energy harvesting mechanisms to address these challenges and enhance operational sustainability. The primary objectives include analyzing various energy harvesting techniques suitable for IoT sensors, optimizing power management protocols, and evaluating the combined system's performance in real-world conditions. A mixed-methods research design was employed, combining both experimental and analytical approaches. The experimental component involved developing prototype sensor nodes equipped with photovoltaic, piezoelectric, and thermoelectric energy harvesting modules, integrated with low-power microcontrollers specifically designed for IoT applications such as the MSP430 and ESP32 platforms. The population of the study consisted of 50 sensor node prototypes fabricated for performance testing, with a stratified sampling technique used to select subsets for field evaluation based on energy harvesting modality. Data collection instruments included power consumption analyzers, environmental sensors, and data loggers to monitor energy generation, storage, and sensor operation over a six-month period in varied environmental conditions. Quantitative data analysis was conducted using regression analysis to determine the relationship between environmental variables and energy harvesting efficiency, and ANOVA tested the differences in energy output among different harvesting techniques. Additionally, a life cycle cost analysis compared the operational costs of energy-harvesting nodes against battery-powered equivalents. System performance metrics such as energy balance, operational uptime, and sensor accuracy were evaluated through descriptive statistics and hypothesis testing to assess the feasibility of sustained operation with minimal maintenance. The study also employed the Technology Acceptance Model (TAM) as a theoretical framework to evaluate user acceptance of energy-harvesting sensor nodes, complemented by the Diffusion of Innovations theory to examine adoption potential in smart environment deployments. The anticipated findings suggest that energy harvesting-enabled sensor nodes can achieve a 60-80% increase in operational lifespan compared to traditional battery-powered sensors, with photovoltaic mechanisms exhibiting the highest energy conversion efficiency under optimal light conditions, while piezoelectric and thermoelectric systems provided more stability in low-light and variable thermal environments. The integrated power management protocols are expected to optimize energy utilization, ensuring continuous operation with negligible downtime. The comparative analysis is projected to reveal significant cost savings over the lifespan of the devices, with energy harvesting reducing the frequency of battery replacement or recharging. This study makes a substantial contribution to the existing body of knowledge by providing a comprehensive assessment of multiple energy harvesting modalities tailored for IoT sensor nodes, proposing an optimized hybrid energy management system, and establishing performance benchmarks for sustainable IoT deployment. The research underscores the potential for energy harvesting to revolutionize IoT sensor network sustainability, significantly reducing operational costs and environmental impact. The main conclusion advocates for the widespread adoption of energy-harvesting sensor nodes in diverse applications, especially in remote or environmentally sensitive areas, coupled with guidelines for scalability and deployment. Recommendations include further research into novel materials for improved harvesting efficiency, integration of energy-aware routing protocols, and exploration of machine learning algorithms for predictive energy management. Future studies should also examine long-term field performance in dynamic environmental conditions, considering the implications of climate change on energy harvesting efficacy.