Energy-Constrained Deployment of Learning Models on Distributed Research Sensors

Abstract

Edge sensors must trade the convenience of local AI inference against limited battery life and intermittent connections. This gap has consequences for interpreting performance outside carefully curated evaluation settings. The planned investigation seeks to identify operating conditions in which local prediction is preferable to delayed cloud processing. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The protocol specifies a discrete-event experiment using simulated energy budgets and repeatable sensor sampling patterns. Comparators will include continuous edge inference, compressed models and scheduled cloud offloading. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are energy per valid inference, end-to-end delay, missed anomalies and buffering requirements. Potential biases include connectivity losses, battery ageing, temperature effects and uncertainty in simulated demand. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].