Intelligent Sensing Framework: Near-Sensor Machine Learning for Efficient Data Transmission

Applications in the Internet of Things (IoT) utilize machine learning (ML) 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 framework to equip sensing systems with intelligent data transmission capabilities by integrating a highly efficient ML 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 framework is orthogonal to other IoT frameworks and can be considered as a plug-in 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 framework has the potential to significantly reduce data output from sensors, benefiting a wide range of IoT applications.