Wireless sensing systems often transmit highly redundant observations, making communication a bottleneck for low-power devices. We present Memento, a communication system that exploits recurring content by transmitting observations by reference. The sender embeds each observation and searches a shared vector table stored in persistent memory. When it finds a sufficiently similar prior observation, it sends a compact reference; otherwise, it sends the observation itself and adds it to the table. This design shifts work from wireless communication to local computation and storage, without requiring a fixed task-specific model. We implement Memento on a microcontroller with an SD card and evaluate it on three image datasets and one radar dataset. Memento provides favorable accuracy-communication and accuracy-energy tradeoffs against compression-based and task-oriented baselines. At high-accuracy operating points, it matches cloud classification accuracy on three workloads while using 5.5 to 27.1 times less energy, and comes within 0.14 percentage points on a fourth while using 14.7 times less energy. These results show that retrieval over persistent local memory can reduce communication costs in recurrent sensing workloads.

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