Applications in the Internet of Things (IoT) utilize machine learning to analyze sensor-generated data. However, a major challenge lies in the lack of targeted intelligence in current sensing systems, leading to vast data generation and increased computational and communication costs. To address this challenge, we propose a novel sensing module to equip sensing frameworks with intelligent data transmission capabilities by integrating a highly efficient machine learning model placed near the sensor. This model provides prompt feedback for the sensing system to transmit only valuable data while discarding irrelevant information by regulating the frequency of data transmission. The near-sensor model is quantized and optimized for real-time sensor control. To enhance the framework's performance, the training process is customized and a "lazy" sensor deactivation strategy utilizing temporal information is introduced. The suggested method is orthogonal to other IoT frameworks and can be considered as a plugin for selective data transmission. The framework is implemented, encompassing both software and hardware components. The experiments demonstrate that the framework utilizing the suggested module achieves over 85% system efficiency in terms of energy consumption and storage, with negligible impact on performance. This methodology has the potential to significantly reduce data output from sensors, benefiting a wide range of IoT applications.

翻译：物联网应用利用机器学习分析传感器生成的数据。然而，主要挑战在于当前传感系统缺乏目标智能性，导致大量数据生成以及计算与通信成本增加。针对这一挑战，我们提出一种新型传感模块，通过集成置于传感器附近的高效机器学习模型，为传感框架赋予智能数据传输能力。该模型通过调节数据传输频率，为传感系统提供即时反馈，使其仅传输有价值数据，同时丢弃无关信息。该近传感器模型经过量化和优化，可实现实时传感器控制。为增强框架性能，我们定制了训练过程，并引入了利用时间信息的"惰性"传感器停用策略。所提方法与其它物联网框架正交，可作为选择性数据传输的插件。我们实现了包含软件和硬件组件的完整框架。实验表明，采用该模块的框架在能耗和存储方面实现了超过85%的系统效率，且对性能影响可忽略不计。该方法能显著减少传感器数据输出，使众多物联网应用受益。