
During a three-month period, Michael More developed and enhanced the SE4CPS/PlantWaterSystem repository, focusing on automated sensor data acquisition and deployment for IoT-based irrigation. He implemented end-to-end soil moisture data pipelines using Python and integrated real-time scheduling and error handling to ensure reliable data transmission from Raspberry Pi devices to a backend API. Michael containerized deployments with Docker, automated device onboarding, and modularized the codebase for maintainability. He also added a Weather API module and local data storage using SQLite, enabling near real-time monitoring and streamlined provisioning. His work established a scalable, maintainable foundation for field-ready IoT deployments.

Month: 2025-04 | Repository: SE4CPS/PlantWaterSystem | Overview: Delivered embedded Plant Water System enhancements with real-time sensor data collection, scheduled one-minute transmissions, local storage, and backend integration. Added deployment utilities, a Weather API module, and deployment scripts to streamline provisioning and updates. No formal bug fixes reported this month; reliability improvements implemented via robust error handling and transmission retries. Impact: improved data fidelity and near real-time monitoring, enabling proactive irrigation decisions and easier device management. Technologies/skills demonstrated: embedded systems development, real-time scheduling, error handling, secure backend API integration, deployment automation, and Weather API integration.
Month: 2025-04 | Repository: SE4CPS/PlantWaterSystem | Overview: Delivered embedded Plant Water System enhancements with real-time sensor data collection, scheduled one-minute transmissions, local storage, and backend integration. Added deployment utilities, a Weather API module, and deployment scripts to streamline provisioning and updates. No formal bug fixes reported this month; reliability improvements implemented via robust error handling and transmission retries. Impact: improved data fidelity and near real-time monitoring, enabling proactive irrigation decisions and easier device management. Technologies/skills demonstrated: embedded systems development, real-time scheduling, error handling, secure backend API integration, deployment automation, and Weather API integration.
March 2025 monthly summary for SE4CPS/PlantWaterSystem focusing on core improvements, deployment readiness, and maintainability across devices. The work lays the foundation for scalable onboarding, reliable telemetry, and streamlined operations.
March 2025 monthly summary for SE4CPS/PlantWaterSystem focusing on core improvements, deployment readiness, and maintainability across devices. The work lays the foundation for scalable onboarding, reliable telemetry, and streamlined operations.
February 2025 (SE4CPS/PlantWaterSystem) focused on delivering end-to-end sensor data capture from soil moisture sensors and automating Raspberry Pi deployments to accelerate field-ready deployments. Key outcomes include a robust soil moisture data pipeline integrated with the backend API, and a repeatable, documented Raspberry Pi onboarding process with automation scripts and I2C enablement. While no major bugs were opened, the work established a scalable edge-to-backend data flow and reduced manual setup time significantly. The initiatives improve data-driven irrigation decisions and operational efficiency for field deployments, with measurable improvements in deployment speed and system reliability.
February 2025 (SE4CPS/PlantWaterSystem) focused on delivering end-to-end sensor data capture from soil moisture sensors and automating Raspberry Pi deployments to accelerate field-ready deployments. Key outcomes include a robust soil moisture data pipeline integrated with the backend API, and a repeatable, documented Raspberry Pi onboarding process with automation scripts and I2C enablement. While no major bugs were opened, the work established a scalable edge-to-backend data flow and reduced manual setup time significantly. The initiatives improve data-driven irrigation decisions and operational efficiency for field deployments, with measurable improvements in deployment speed and system reliability.
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