Device-cloud collaborative inference is often necessary for resource-constrained IoT devices that cannot support full on-device models. To minimize bandwidth and support concurrency, existing methods typically compress images before transmission. However, these approaches often ignore the significant overhead of decoding native JPEG camera output, especially for high-resolution frames. Our measurements show that the on-device (Raspberry Pi 4B) decoding overhead for 700KB JPEG format is$\sim$14.4x the latency of on-cloud (GeForce RTX 3090) meter recognition inference. To reduce on-device decoding overhead, we design DC Camera, which is built upon a JPEG camera and leverages partial JPEG decoding to efficiently extract DC features from high-resolution images, significantly mitigating on-device image processing overhead. These DC features can preserve structural information better than conventional downsampled images. We utilize DC Camera to implement fast meter recognition system and deploy the system in material science laboratory to monitor multiple meters. Our evaluation demonstrates that compared to state-of-the-art (SOTA) methods, DC Camera can reduce on-device computation overhead by$\sim$5.8x and decrease transmission volume by$\sim$90.9x, without inference accuracy degradation.

In recent years, the swift expansion of Internet of Things (IoT) applications has been notable. However, developing a comprehensive IoT application is highly challenging for non-expert developers due to the highly diverse characteristics of embedded operating systems. LLM-based methods provide a paradigm for code generation through natural language, which can greatly simplify and accelerate the development of IoT applications. While promising, existing works have failed to account for the specific characteristics of embedded operating system, resulting in the lower quality of generated IoT code. In this paper, we present IoTPilot, an LLM-driven embedded IoT programming tool. We have observed that the conflicts between LLM internal APIs/headers and external OS-specific APIs/headers are key factors leading to the low quality of generated embedded IoT applications. Thus, we introduce two effective self-thinking chains to integrate internal LLM knowledge with external documentation, addressing conflicts in APIs and headers. We provide embedded IoT benchmarks (IoTEval), which are built on RIOT, Zephyr, Contiki and FreeRTOS. Results show that IoTPilot can improve the performance of IoT code generation on all the three embedded OSes compared with existing state-of-the-art (SOTA) methods.

In recent years, the swift expansion of Internet of Things (IoT) applications has been notable. However, developing a comprehensive IoT application is highly challenging for non-expert developers due to the highly diverse characteristics of embedded operating systems. The LLM-based approach shows promise in generating code from natural language, but its performance in IoT code generation is poor. This stems from the LLM's insufficient understanding of the embedded IoT code context, leading to missed and conflicting OS-specific APIs. In this paper, we present IoTPilot, a LLM-driven multi-agent IoT programming framework. We develop a clustering-based progressive RAG strategy and auto-calibrating self-debug mechanism to enhance the quality of generated IoT applications.