LION: A Learned Index for On-Device Sensor Data Management

With the rapid advancement of edge device performance, efficient on-device data management and processing have become critical. Transmitting data to edge servers introduces network overhead and a delayed data problem. However, modern edge devices now offer enhanced computing power and increased memory/storage capacity, enabling local data processing to address these challenges. In this work, we propose LION, a learned index for ondevice sensor data management. LION employs the OptimalPLR algorithm to train a linear model for each node. LION reduces training overhead by deferring the computation of the slope and intercept for each data node. Furthermore, to overcome limitations imposed by the maximum error bound, LION leverages Scope, a simple indirection table that improves lookup efficiency. Experimental results demonstrate that LION outperforms other indexing methods on both synthetic and real-world workloads.