This paper proposes a cost-effective five-component discrete capacitive pre-charging circuit designed to mitigate charge redistribution effects in Analog-to-Digital Converter (ADC) inputs, particularly for low-cost embedded applications involving multiplexed high-output-resistance sources. The paper presents an analysis and experimental validation of this approach, comparing its performance against traditional methods like grounding or leaving unused multiplexer inputs floating. The proposed solution leverages external components (two capacitors and three switches) and multiplexer features to pre-charge ADC inputs to approximately half the reference voltage, which could be taken directly from the multiplexer supply rail, significantly reducing transient glitches and settling times. The experimental results demonstrate a clear improvement, achieving settling times up to 1.4 µs shorter than conventional approaches during specific multiplexer transitions. Component selection guidelines are outlined, including compensation capacitor sizing and transistor choice, addressing practical concerns such as charge injection effects. Despite certain experimental constraints noted during testing, the developed discrete pre-charging method consistently exhibited substantial performance gains. Our findings confirm that this practical, minimal-component strategy effectively addresses charge redistribution challenges, presenting an efficient solution for enhancing ADC input accuracy and response speed in resource-limited embedded sensor systems.

Battery-powered Internet of Things (IoT) sensor nodes for environmental monitoring face strict energy constraints, particularly when employing high-consumption sensors such as particulate matter sensors or gas analyzers. Extending operational lifetime without sacrificing measurement reliability remains a key challenge for large-scale air-quality monitoring deployments. This paper proposes a digital-twin-assisted adaptive sensing algorithm for reducing energy consumption by dynamically optimizing sensor usage for Indoor Air Quality (IAQ) monitoring system. The system consists of distributed sensing nodes and a central station that maintains digital twins to evaluate candidate sensing strategies based on historical data and environmental patterns. Strategies are assessed in terms of energy consumption and measurement fidelity and deployed only when a measurable improvement is achieved. The approach is evaluated across mobile and stationary sensor configurations used for monitoring indoor air quality in university laboratories while educational and research activities are carried out. For stationary nodes, clustering-based scheduling reduces the activation of high-power sensors, while for mobile nodes, variation-based triggering exploits correlations between equivalent and reference CO₂ measurements to limit energy-intensive sensing. Results demonstrate energy savings of up to approximately 70% while maintaining acceptable measurement fidelity. The findings show that reduced sensing can be used for system initialization, while digital twin evaluation enables reliable transition to adaptive sensing under suitable conditions.