Can LLMs Be Effective Sensor Processing Copilots?

Effective sensor data processing is critical for cyber-physical and Internet of Things (IoT) systems, but often requires specialized expertise. While large language models (LLMs) show promise as autonomous copilots for sensor processing, their capabilities remain underexplored. We introduce SensorBench, the first comprehensive benchmark for evaluating LLMs across diverse real-world sensor datasets and tasks. SensorBench evaluates three paradigms for leveraging LLMs in sensing tasks: tool-augmented coding (TAC), standalone coding (SAC), and direct answer (DA). We evaluate 8 leading LLM variants, including 2 large reasoning models (LRMs) and 2 domain-specific LLMs, providing a structured reference for absolute performance, latency, and resource requirements. Our analysis reveals that: 1) TAC significantly outperforms SAC and DA; 2) LLMs excel at simple tasks but consistently underperform domain experts on compositional tasks requiring parameter tuning and multistep reasoning; and 3) the reasoning mechanism introduced in LRMs does not yield substantial performance gains. To improve the performance, we explore four prompting strategies and fine-tuning approaches (using our newly released sensor-processing corpus). The results show that self-verification prompting proves most effective, outperforming other methods simultaneously in 48% of tasks, while fine-tuning yields marginal gains. Our analysis suggests that more sophisticated interaction frameworks, such as signal-level self-verification, may bridge the gap to human expert-level performance. This benchmark provides a foundation for evaluating and improving LLMs in sensing applications https://github.com/nesl/LLM_sensor_processing