Self-powered intelligent indoor environmental sensor (SmartSensor)

Result

A self-powered sensor system was developed and integrated with a photovoltaic (PV) solar module to enable autonomous operation without reliance on an external power source. The harvested solar energy was used to power the sensing, data acquisition, and wireless communication processes while surplus energy was stored in a supercapacitor to sustain operation during periods of low irradiance. The sensor continuously monitored the supercapacitor voltage and recorded measurements over an extended period under real operating conditions. The collected dataset provides valuable insight into the charging and discharging behavior of the energy storage system, the stability of the self-powered operation, and the effectiveness of the adaptive measurement and transmission strategy. These measurements form the basis for further analysis of the system's energy management performance, operational reliability, and long-term feasibility for autonomous sensing applications.

Description

The Internet of Things (IoT) is being transformed by self-powered sensors, enabling continuous data collection without frequent battery replacements. These sensors harvest energy from their environment, making IoT systems more sustainable and crucial for applications like environmental monitoring, healthcare, and smart infrastructure. Their energy-efficient design addresses global challenges such as climate change and resource management.

A key advancement is the development of intelligent IoT systems that adapt to environmental conditions. In self-powered IoT devices, where energy harvesting depends on fluctuating environmental factors such as light availability, sensors can be programmed to optimize data processing based on the prevailing energy profile [1]. For example, in high light, they maximize data transmission, while conserving energy in low-light conditions to extend operational life.

Using perovskite solar cells as energy providers for IoT sensors offers a significant advantage due to their high efficiency, flexibility, and ability to generate power under low-light conditions[2]. Unlike traditional silicon-based cells, perovskites are more versatile and can be tailored for indoor use, making them ideal for powering sensors in smart buildings and homes, reducing the need for external energy sources and maintenance, and supporting large-scale IoT deployments.

In the first phase of our work, we will focus on measuring and analyzing the environmental light spectrum in the target settings to identify the most suitable perovskite composition for efficient light harvesting. By studying the specific light conditions, such as the intensity and wavelength distribution in indoor environment, we aim to optimize the selection of perovskite materials with right bandgaps that are well-matched to the available light. This step is critical for maximizing the energy conversion efficiency of our solar cells, ensuring they can effectively capture and utilize the ambient light, especially under low-light conditions typical of indoor spaces or shaded areas. With this data, we will be able to fine-tune the material properties to achieve optimal performance for self-powered IoT devices. To enhance the long-term stability of these cells, we will use encapsulation to protect them against moisture and oxygen, common factors that contribute to degradation[3]. Additionally, we will test these encapsulated cells under controlled harsh conditions using our in-house stability setup, allowing us to evaluate their performance over extended periods and estimate their lifespan in practical applications.

In the second phase of the project, we will evaluate the performance of the perovskite solar cells under indoor light conditions to quantify the amount of power they can generate and assess any efficiency losses over time. By measuring the initial power output and monitoring its degradation under controlled light environments, we can determine how effectively the solar cells convert indoor lighting into usable energy. This data will allow us to calculate the required active area of the perovskite solar cells to ensure they can consistently provide enough power for the low-energy demands of the environmental sensor. We will also analyze the long-term stability of the cells under operational conditions, identifying potential factors contributing to efficiency loss, such as light-induced degradation or environmental stressors, which will help us refine the design to optimize both energy output and durability over time.

In the third phase of this project, we will use ambient light-harvesting perovskite solar cells to power environmental sensors and train them to manage energy usage efficiently using machine learning techniques. The sensors will operate in various light conditions, collecting data to optimize energy consumption based on real-time light availability. By integrating an artificial neural network (ANN), particularly using long short-term memory (LSTM) units, the sensors will learn to dynamically adjust their power usage and processing activities. This intelligent energy management system will ensure continuous operation of the self-powered sensors, making them highly suitable for long-term environmental monitoring and other IoT applications requiring efficient, autonomous operation.

Finally, we will develop a working prototype of the intelligent self-powered environmental sensor and install it at various locations within ZHAW. The sensor will collect temperature and humidity data from the environment, enabling comprehensive monitoring of local conditions. This will allow us to test the system under 100% real operational conditions, assessing its energy performance and functionality in everyday use.

Key data