Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems.

Continuous glucose monitoring (CGM) plays a central role in modern diabetes management, yet CGM data are often confined within proprietary manufacturer ecosystems, limiting interoperability and reuse. This paper presents OpenGluco, an open-source and provider-independent platform designed to unify CGM data from multiple commercial systems, including Abbott Freestyle Libre, Dexcom, and Medtronic. OpenGluco implements a modular server architecture that abstracts vendor-specific data access, normalizes heterogeneous glucose time series, and exposes standardized access through an application programming interface designed to support future scalability. The platform supports individual users as well as institutional and research deployments. Performance evaluation demonstrates reliable data ingestion, consistent API responsiveness within the evaluated deployment environment, and robust handling of heterogeneous sampling characteristics. Validation of data normalization shows preservation of clinically relevant metrics such as Time in Range. By emphasizing interoperability, user-authorized data access, and open-source transparency, OpenGluco provides a flexible foundation for clinical monitoring, education, and future analytics-driven decision-support applications in diabetes care.