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This work introduces a novel perspective in the study of smart sensors technology. The final aim is to develop a new methodology that supports the conception, design and implementation of complex sensor-based systems in a more structured and information-oriented way. A smart sensor can be considered as a hardware/software transducer able to bring the measured physical signal(s) at an application level. However, when viewed through the lens of artificial intelligence, sensor ‘smartness' appears to stay in between merely transduction and complex post-processing, with the boundary purposely left blurry and undetermined. Thanks to the recent literature findings on the so-called ‘holonic systems', a more precise characterization and modeling of the smart sensor is provided. A ‘holon' is a bio-inspired conceptual and computational entity that, as a cell in a living organism, plays the two roles of a part and a whole at the same time. To bring the right evidence of to the advantages of the holonic approach, an example smart application and a related prototype implementation for the disambiguation of low-cost gas sensor responses is shown. The proposed approach unravels the inherent complexity of the disambiguation problem by means of a scalable architecture entirely based on holonic-inspired criteria. Furthermore, the overall setup is economically competitive with other high-selective (hence high-cost) sensor-based solutions.