Energy-Constrained Deployment of Learning Models on Distributed Research Sensors: Evidence, Failure Modes and Deployment Trade-offs

Abstract

Edge sensors must trade the convenience of local AI inference against limited battery life and intermittent connections. The central research objective is to identify operating conditions in which local prediction is preferable to delayed cloud processing. Previous work provides context for sampling, input validation and study boundaries [3–7]. To answer the question, the study proposes a discrete-event experiment using simulated energy budgets and repeatable sensor sampling patterns. Related enterprise-storage studies inform the assessment of operational reliability [1] and the deployment assumptions adopted for system-level comparison [2]. Under matched evaluation conditions, the protocol compares continuous edge inference, compressed models and scheduled cloud offloading. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The evidence-mapping component examines why a successful result in one technical environment may fail elsewhere. Performance will be judged using energy per valid inference, end-to-end delay, missed anomalies and buffering requirements. Interpretation will account for connectivity losses, battery ageing, temperature effects and uncertainty in simulated demand. Stress tests would distinguish outright failures, reduced confidence and cases where a simpler baseline is preferable. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].