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For future crewed missions that could last years with limited ground support, the environmental control and life support system (ECLSS) will likely evolve to meet new, more stringent reliability and autonomy requirements. In this work, we focus on improving the performance of the environmental monitoring and anomaly detection systems using Markov decision process and active sensing. We exploit actively moving sensors to develop a novel sensing architecture and supporting analytics, termed Active environmental Monitoring and Anomaly Search System (AMASS). We design a Dynamic Value Iteration policy to solve the path planning problem for the moving sensors in a dynamic environment. To test and validate AMASS, we developed a series of computational experiments for fire search, and we assessed the performance against three metrics: (1) anomaly detection time lag, (2) source location uncertainty, and (3) state estimation error. The results demonstrate that: AMASS provides 10~15 times better performance than the traditional fixed sensor monitoring and detection strategy; ventilation in the monitored environment affects the performance by 6~40 times for any monitoring architecture with fixed or moving sensors; the monitoring performance cannot be fully reflected in a monolithic, single metric, but should include different metrics for the timeliness and spatial resolution of the detection function.

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