The rapid expansion of cyber-physical systems (CPS) has intensified the need for scalable and adaptive mechanisms to collect heterogeneous environmental data from numerous unstable external sources. Traditional serverless orchestration frameworks, while elastic and cost- efficient, lack runtime adaptability and feedback-awareness, leading to inefficiencies under dynamic API conditions. This paper presents a novel adaptive orchestration model for serverless data collection pipelines, driven by metadata configuration and continuous feedback control. The proposed system integrates AWS-based components (Lambda, EventBridge, SQS, S3, Athena, MongoDB) to enable autonomous management of data collection processes from OpenAQ, NOAA, NASA GES DISC, and ESA
Copernicus. Adaptive behavior has been achieved through feedback-based health scoring and metadata-driven reconfiguration, improving resilience to vendor instability, API schema changes, and rate-limit fluctuations. Experimental validation has demonstrated a 40% reduction in failed invocations, a 22% latency improvement, and a 17% decrease in operational costs compared to static orchestration approaches. The results confirm the feasibility of fully adaptive, serverless data orchestration and establish the groundwork for future AI-assisted autonomous orchestration in heterogeneous CPS environments.
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