资源受限IoT设备的有效在线分类和跟踪
Efficient Online Classification and Tracking on Resource-constrained IoT Devices
摘要：
在智能IoT设备上越来越需要及时处理，这导致在IoT设备上直接实现信息处理任务以节省带宽并保证隐私。特别地，以连续形式监视和跟踪所观察到的信号是各种近实时处理IoT设备的常见任务，诸如在灵巧家庭、身体区域和环境感测应用中。然而，这些系统可能是低成本的资源受限的嵌入式系统，配备有紧凑的存储器空间，从而存储连续信号的完整信息状态的能力是有限的。因此，在本文中，我们开发了高效及时处理嵌入式系统的解决方案，用于在线分类和跟踪具有紧凑存储空间的连续信号。特别是，我们专注于智能插头的应用，能够以独立的方式及时分类电器类型和跟踪电器行为。我们使用低成本的Arduino平台实现了一个智能插头原型，具有少量的内存空间，以演示以下及时处理操作：（1）学习和分类与连续功耗信号相关的模式，以及（2）使用小的本地内存空间跟踪信号模式的发生。此外，我们的系统设计也足够通用，可以及时监控和跟踪其他资源受限的物联网设备中的应用程序。
英文摘要：
Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this paper, we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals, and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
