Design and Implementation of a Low-Power IoT Sensor Node with Enhanced Energy Efficiency

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) applications underscores a critical need for energy-efficient sensor nodes capable of sustainable long-term operation, especially in remote or inaccessible environments where frequent maintenance is impractical. Despite advances in IoT technology, many sensor nodes suffer from high power consumption due to inefficient hardware components and suboptimal energy management strategies, thereby limiting their operational lifespan and increasing maintenance costs. This study aims to design, implement, and empirically evaluate a low-power IoT sensor node with enhanced energy efficiency, addressing the pressing challenge of prolonging device lifetime without compromising data accuracy or network reliability. The specific objectives include developing an optimized hardware architecture leveraging ultra-low-power microcontrollers, integrating energy-harvesting modules to supplement power sources, implementing adaptive power management algorithms, and assessing the system's energy consumption under various operational scenarios. The research adopts a mixed-methods approach, comprising the design and development phases of hardware and software prototypes, followed by quantitative evaluation through experimental testing. The target population involves IoT sensor modules used in environmental monitoring networks, with a sample size of 30 independently developed sensor nodes subjected to controlled laboratory tests and field deployments across different environmental conditions. Data collection instruments include power measurement tools such as digital oscilloscopes and current meters, alongside custom logging software to record energy consumption metrics. The analysis employs descriptive statistics to quantify energy savings, paired t-tests to compare power consumption before and after algorithm implementation, and regression analysis to identify factors influencing energy efficiency. Systematic thematic analysis is applied to interpret qualitative feedback from field deployment observations, and finite element modeling is used to simulate energy harvesting potential and optimize hardware configurations. Expected findings anticipate a significant reduction, estimated at 40-60%, in average power consumption of the sensor nodes equipped with the novel energy management system, extending operational lifespan by up to 150% under typical environmental monitoring workloads. The integration of energy harvesting is projected to further sustain node functionality in power-scarce settings. The results are expected to demonstrate that a combination of hardware optimization and intelligent software algorithms effectively enhances overall energy efficiency without adversely affecting sensor performance or data fidelity. The study contributes to knowledge by establishing a comprehensive framework for designing energy-conscious IoT sensor nodes, advancing the theoretical understanding of power management strategies in resource-constrained environments, and providing practical guidelines for scalable deployment. The main conclusion emphasizes that multi-layered energy optimization, incorporating hardware innovations and adaptive power control algorithms, is vital for sustainable IoT sensor networks. Policy recommendations advocate for the adoption of integrated energy harvesting modules and low-power microcontrollers in future IoT device designs. The study also suggests avenues for further research, including exploring machine learning techniques to dynamically optimize power consumption in real-time and investigating novel materials for more efficient energy harvesting. Ultimately, this research advances the development of autonomous, long-lasting IoT sensor nodes, thereby facilitating more sustainable and cost-effective deployment of IoT ecosystems across diverse sectors.