InTec框架通过三层次分布计算，显著降低边缘AI系统延迟与能耗。
InTec: integrated things-edge computing: a framework for distributing machine learning pipelines in edge AI systems
- 将机器学习任务分层部署于终端、边缘与云端，实现数据就近处理。
- 在MHEALTH数据集上实现响应时间下降81.56%，云端能耗降25.83%。
- 适合追求低延迟、高能效的智能家庭与可穿戴设备应用。
随着物联网（IoT）的快速发展，传感器、智能手机和可穿戴设备已深度融入日常生活，支撑智能家居、医疗健康和智能交通等智慧应用。然而，传统基于云的机器学习（ML）框架面临延迟和带宽瓶颈。现有将部分ML流程迁移至边缘与云层的方法未能彻底解决此问题，常因边缘设备算力不足导致响应时间更长、网络拥塞加剧。为此，本文提出InTec（Integrated Things Edge Computing）框架，创新性地利用三层次架构，将ML任务智能分配至终端（Things）、边缘（Edge）和云（Cloud）层。该方法实现数据生成点的实时处理，显著降低延迟、优化网络流量并提升系统可靠性。基于MHEALTH数据集的人体运动检测实验表明：响应时间减少81.56%，网络流量下降10.92%，吞吐量提升9.82%，边缘能耗降低21.86%，云端能耗减少25.83%。InTec为可扩展、高效响应且节能的物联网应用树立新基准，展现了其在边缘智能（EI）系统中革新ML流程的巨大潜力。
原文摘要 · Abstract (English)
With the rapid expansion of the Internet of Things (IoT), sensors, smartphones, and wearables have become integral to daily life, powering smart applications in home automation, healthcare, and intelligent transportation. However, these advancements face significant challenges due to latency and bandwidth constraints imposed by traditional cloud based machine learning (ML) frameworks. The need for innovative solutions is evident as cloud computing struggles with increased latency and network congestion. Previous attempts to offload parts of the ML pipeline to edge and cloud layers have yet to fully resolve these issues, often worsening system response times and network congestion due to the computational limitations of edge devices. In response to these challenges, this study introduces the InTec (Integrated Things Edge Computing) framework, a groundbreaking innovation in IoT architecture. Unlike existing methods, InTec fully leverages the potential of a three tier architecture by strategically distributing ML tasks across the Things, Edge, and Cloud layers. This comprehensive approach enables real time data processing at the point of data generation, significantly reducing latency, optimizing network traffic, and enhancing system reliability. InTec effectiveness is validated through empirical evaluation using the MHEALTH dataset for human motion detection in smart homes, demonstrating notable improvements in key metrics: an 81.56 percent reduction in response time, a 10.92 percent decrease in network traffic, a 9.82 percent improvement in throughput, a 21.86 percent reduction in edge energy consumption, and a 25.83 percent reduction in cloud energy consumption. These advancements establish InTec as a new benchmark for scalable, responsive, and energy efficient IoT applications, demonstrating its potential to revolutionize how the ML pipeline is integrated into Edge AI (EI) systems.
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