Environment-driven Communication in Battery-free Smart Buildings

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2021-04-22 DOI:10.1145/3448739
Mauro Piva, Andrea Coletta, G. Maselli, J. Stankovic
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Abstract

Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.
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无电池智能建筑中的环境驱动通信
近年来,人们设计和开发了几种无线和无电池的智能设备。这些设备利用基于RFID反向散射的计算和传输。尽管单个设备可以高效运行,但它们的共存需要控制,因为它们根据与环境的交互而具有广泛不同的通信需求。设计能够动态适应当前设备运行的高效通信协议是一个现有工作无法很好解决的新问题。在本文中,我们提出了一种新的通信协议,称为ReLEDF,它动态地发现智能建筑中的设备及其活动和非活动状态,以及激活时它们当前的通信行为(通过基于学习的机制)和调度传输插槽(通过基于最早截止日期优先(EDF)的机制)以适应不同的数据传输需求。结合学习和调度引入了标签饥饿问题,因此我们也提出了一种新的模式改变调度方法。广泛的模拟清楚地显示了使用ReLEDF的好处,它在典型的智能家居场景中成功地提供了超过95%的新数据样本,其中多达150个异构智能设备,优于相关解决方案。通过实际实验验证了该方法的适用性和仿真结果的正确性。
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CiteScore
5.20
自引率
3.70%
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0
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