A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission

TL;DR

This paper introduces a plug-in tiny AI module for IoT sensors that intelligently filters data at the source, reducing transmission costs and energy consumption while maintaining system performance.

Contribution

The paper presents a novel near-sensor machine learning module that enables selective data transmission, optimized for real-time control and integrated as a plugin in sensing frameworks.

Findings

Achieves over 85% system efficiency in energy and storage.

Reduces sensor data output significantly with minimal performance impact.

Demonstrates effectiveness across IoT applications.

Abstract

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.