Design and Implementation of a Low-Power Wireless Sensor Node for Smart Agriculture

Thesis Abstract

The burgeoning demand for sustainable agricultural practices necessitates the development of efficient, low-power sensor technologies capable of supporting real-time environmental monitoring in smart farming systems. Despite advancements in wireless sensor networks (WSNs), existing solutions often suffer from high power consumption, limited scalability, and environmental resilience, thereby impeding widespread adoption and limiting their practical utility in diverse agricultural contexts. This study seeks to address these challenges by designing, implementing, and evaluating a low-power wireless sensor node tailored specifically for smart agriculture applications, with the overarching goal of enhancing sensor network longevity and reliability. The primary objectives include developing an energy-efficient hardware architecture, implementing optimized communication protocols, and assessing the sensor node's performance under controlled and field conditions. Adopting a mixed-method research design, the study combines empirical experimentation with quantitative data analysis to ensure comprehensive evaluation. The hardware development phase employed a design science approach, focusing on selecting ultra-low-power microcontrollers, energy harvesting components, and low-power radio transceivers. A prototype sensor node was constructed using components such as the MSP430 microcontroller, thermoelectric generators for energy harvesting, and LoRaWAN modules for long-range wireless communication. The sample universe consisted of 50 sensor nodes deployed across three distinct farm plots exhibiting varied crop types, soil conditions, and geographical features. A stratified random sampling technique was used to select 15 nodes for intensive performance testing over a monitoring period of six months. Data collection instruments included data loggers, environmental sensors (soil moisture, temperature, humidity), and power consumption measurement tools. Quantitative data comprised metrics such as power consumption levels, data transmission success rate, network latency, and energy harvesting efficiency. To validate the reliability and accuracy of measurement instruments, calibration procedures and pilot testing were conducted prior to data collection. Data analysis involved statistical techniques such as descriptive statistics, regression analysis to determine relationships between environmental variables and power consumption, and ANOVA to assess differences in performance across diverse field conditions. Additionally, thematic analysis was applied to qualitative logs and user feedback regarding the sensor nodes’ ease of deployment and maintenance. The anticipated key findings include significant reductions in energy consumption compared to existing sensor solutions, with an average power usage below 10 milliwatts per node, enabling extended deployment periods exceeding six months without battery replacement. The integration of energy harvesting is expected to contribute substantially (at least 30%) to self-sustaining operation in various outdoor environments. The sensor nodes are projected to demonstrate high data transmission success rates (above 95%), low latency (under 2 seconds), and resilience to environmental factors such as dust, moisture, and temperature variations. The findings will also reveal vital insights into the influence of environmental and infrastructural variables on power efficiency and network reliability. This study makes a novel contribution to knowledge by providing a practical blueprint for designing ultra-low-power, energy-harvesting wireless sensors tailored specifically for distributed agriculture environments. It extends existing WSN frameworks by integrating sustainable power solutions and customized communication protocols tailored to farm operational needs. The research underscores the significance of multidisciplinary approaches combining electronics, wireless communication, and agricultural science to optimize sensor network designs for real-world applications. Concluding, the study affirms that the developed sensor nodes are feasible, scalable, and capable of supporting robust, long-duration smart farming systems. Policy implications include recommendations for integrating energy-efficient sensor technology into national agricultural extension services and rural development programs. Future research avenues include exploring machine learning algorithms for predictive analytics based on sensor data, developing sensor nodes for additional environmental parameters, and refining energy harvesting capabilities to adapt to seasonal variations. Overall, this work advances the deployment of sustainable, low-power sensor networks, thereby contributing significantly to the realization of precision agriculture and resource-efficient farming practices.