Energy-aware active chemical sensing

We propose an adaptive sensing framework for metal-oxide (MOX) sensors that seeks to minimize energy consumption through temperature modulation. Our approach generates temperature programs by means of an active-sensing strategy combined with an objective function that penalizes power consumption. The problem is modeled as a partially observable Markov decision process (POMDP) and solved with a myopic policy that operates in real time. The policy selects sensing actions (i.e., temperature pulses) that balance misclassification costs (e.g., chemicals identified as the wrong target) and sensing costs (i.e., power consumption). We experimentally validate the method on a ternary chemical discrimination problem, and compare it against a "passive classifier." Our results show that, for a given energy budget, the active-sensing strategy selects temperatures with more discriminatory information than those of the passive classifier by penalizing pulses of higher temperature and longer durations.