Internet-enabled physical devices with "smart" processing capabilities are becoming the tools for understanding the complexity of the global inter-connected world we inhabit. The Internet of Things (IoT) churns tremendous amounts of data flooding from devices scattered across multiple locations to the processing engines of almost all industry sectors. However, as the number of "things" surpasses the population of the technology-enabled world, real-time processing and energy-efficiency are great challenges of the big data era transitioning to IoT. In this article, we introduce a lightweight adaptive monitoring framework suitable for smart IoT devices with limited processing capabilities. Our framework, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the monitoring stream. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing on cloud computing and big data services. Experiments on real-world data from cloud services, internet security services, wearables and intelligent transportation services, show that our framework achieves a balance between efficiency and accuracy. Specifically, our framework reduces data volume by 74%, energy consumption by at least 71%, while achieving a greater than 89% accuracy.

中文翻译：

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物联网的低成本自适应监控技术

具有“智能”处理能力的互联网物理设备正在成为了解我们所居住的全球互联世界的复杂性的工具。物联网 (IoT) 将大量数据从分散在多个位置的设备传输到几乎所有行业的处理引擎。然而，随着“物”的数量超过技术赋能世界的人口，实时处理和能源效率是大数据时代向物联网过渡的巨大挑战。在本文中，我们介绍了一种适用于处理能力有限的智能物联网设备的轻量级自适应监控框架。我们的框架，根据监测流的当前演变和可变性，以低成本和适当的方式动态调整监测强度和通过网络传播的数据量。通过实现这一点，可以减少能耗和数据量，使物联网设备能够节省电池并简化云计算和大数据服务的处理。对来自云服务、互联网安全服务、可穿戴设备和智能交通服务的真实世界数据的实验表明，我们的框架实现了效率和准确性之间的平衡。具体来说，我们的框架将数据量减少了 74%，能耗至少减少了 71%，同时实现了超过 89% 的准确率。允许物联网设备保存电池并简化云计算和大数据服务的处理。对来自云服务、互联网安全服务、可穿戴设备和智能交通服务的真实世界数据的实验表明，我们的框架实现了效率和准确性之间的平衡。具体来说，我们的框架将数据量减少了 74%，能耗至少减少了 71%，同时实现了超过 89% 的准确率。允许物联网设备保存电池并简化云计算和大数据服务的处理。对来自云服务、互联网安全服务、可穿戴设备和智能交通服务的真实世界数据的实验表明，我们的框架实现了效率和准确性之间的平衡。具体来说，我们的框架将数据量减少了 74%，能耗至少减少了 71%，同时实现了超过 89% 的准确率。