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.

提出了一种新的传感模块，通过在传感器附近集成高效的机器学习模型，为传感框架提供智能数据传输能力，以解决当前传感系统中缺乏定向智能的问题，并通过调节数据传输频率，仅传输有价值的数据并丢弃无关信息。该模型经过量化和优化，用于实时传感器控制，结合定制化的训练过程和利用时间信息的 “懒惰” 传感器停用策略来增强框架性能，通过软件和硬件组件实现。实验证明，利用该模块的框架在能源消耗和存储方面实现了超过 85％的系统效率，并对性能几乎没有影响。该方法有潜力显著减少传感器的数据输出，从而惠及各种物联网应用。