Radio selection and data partitioning for energy-efficient wireless data transfer in real-time IoT applications
Radio selection and data partitioning for energy-efficient wireless data transfer in real-time IoT applications
复制标题
DOI：
10.1016/j.adhoc.2020.102251
复制
发表时间：
2020-10
期刊：
影响因子：
4.8
通讯作者：
Di Mu;M. Sha;K. Kang;Hyungdae Yi
中科院分区：
文献类型：
--
作者：
Di Mu;M. Sha;K. Kang;Hyungdae Yi
实时无线数据传输对于物联网（IoT）应用的重要性正在迅速增加。例如，医生佩戴的智能眼镜需要将实时数据传输到医院信息系统，该系统进行人脸检测和识别，以便在一定的时间内（理想的时间是几百毫秒）与被识别的患者进行实时交互。其他新兴的物联网应用，如结构健康监测、临床监测和工业过程自动化，也需要实时无线数据传输。这些应用对通过无线媒介进行实时和高能效的通信有着至关重要的要求。然而，由于无线媒体固有的不可靠性和时间不可预测性，通过无线媒体高效地支持严格的时间限制是非常具有挑战性的。幸运的是，异构无线电在现代嵌入式设备中的可用性越来越高，为使用多种无线技术来满足实时应用的需求提供了新的机会。在本文中，我们将实时物联网应用的运行时无线电选择和数据划分制定为整数线性规划（ILP）问题，并提出了一种在两个无线电之间选择时快速做出最佳决策的最优算法，一种针对无线电较多平台的启发式算法，以及一种在面临紧迫截止日期时减少截止日期错过率的运行时间算法。
The importance of real-time wireless data transfer is rapidly increasing for Internet of Things (IoT) applications. For example, smart glasses worn by a doctor need to transmit real-time data to a hospital information system, which performs face detection and recognition, for real-time interaction with recognized patients within a certain deadline, which is ideally a few hundred milliseconds. Other emerging IoT applications, e.g., structural health monitoring, clinical monitoring, and industrial process automation, also require real-time wireless data transfer. Those applications have critical demands for real-time and energy-efficient communication through wireless medium. However, it is very challenging to support stringent timing constraints energy-efficiently through wireless medium due to its inherent unreliability and timing-unpredictability. Fortunately, heterogeneous radios are becoming increasingly available in modern embedded devices, offering new opportunities to use multiple wireless technologies to accommodate the needs of real-time applications. In this paper, we formulate the runtime radio selection and data partitioning for real-time IoT applications as an Integer Linear Programming (ILP) problem and present anoptimalalgorithm that makes quick and optimal decisions when selecting between two radios, aheuristicalgorithm for the platforms with more radios, and aruntimealgorithm that reduces deadline miss ratio when facing tight deadlines.