Phase-independent wireless data aggregation via optical in-sensor computing

The data-intensive nature of the Internet of Things (IoT) significantly challenges conventional communication systems. While over-the-air computation integrates communication and computation to reduce data aggregation burdens, it requires strict synchronization of transmitted signals, increasing system overhead. Here, we report an optical in-sensor wireless data aggregation paradigm that exploits photocarrier trapping in defect-dominated persistent photoconductance sensors to reduce dependence on synchronization constraints. Within a single communication window, the system reliably aggregates incoherent optical signals with relative timing offsets exceeding the signal duration by up to 120%, while achieving computation errors below 1.5% normalized mean squared error. This process achieves unbiased integration with pulsed low-frequency readout, indicating its potential for ultra-low power operation. A distributed temperature prediction model based on this paradigm shows faster convergence and competitive accuracy with a simplified architecture, demonstrating effectiveness in distributed edge sensing scenarios. This work provides a promising hardware implementation for low-power data aggregation in large-scale IoT edge networks. Massive connectivity between intelligent devices impose significant challenges on communication systems. Sun et al. report a persistent photoconductance sensor that integrates communication and computation through in-sensor aggregation of incoherent optical signals, enabling energy-efficient wireless data aggregation.