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arXiv 2312.16807cs.NIcs.SYeess.SY
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通过并发泛洪实现高效的干扰图估计

Efficient Interference Graph Estimation via Concurrent Flooding

Shanghai Jiao Tong University(上海交通大学)

Haifeng Jia, Yichen Wei, Zhan Wang, Jiani Jin, Haorui Li, Yibo Pi

AI总结:

本文提出通过并发泛洪与功率控制实现高效的干扰图估计,以解决无线网络中干扰图应用的挑战。

摘要:

传统网络管理方法为测量和数据传输任务分别分配网络资源。重测任务可能会占用数据传输资源并显著降低网络性能。因此,被视为造成大量测量开销的干扰图在无线网络中实际应用具有挑战性。为了解决这一挑战,我们在无线传感器网络中提出将功率作为干扰图估计(IGE)的新维度,并将IGE与并发泛洪相结合,使得IGE可以利用相同的频时资源与泛洪同时进行。通过受控和现实世界的实验,我们展示了通过控制节点的发射功率,在商用现成(COTS)设备上通过并发泛洪高效实现IGE是可行的。我们认为高效的IGE将是现有调度算法实际应用的关键使能因素,假设已知干扰图。

英文摘要:

Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, we propose to use power as a new dimension for interference graph estimation (IGE) and integrate IGE with concurrent flooding such that IGE can be done simultaneously with flooding using the same frequency-time resources. With controlled and real-world experiments, we show that it is feasible to efficiently achieve IGE via concurrent flooding on the commercial off-the-shelf (COTS) devices by controlling the transmit powers of nodes. We believe that efficient IGE would be a key enabler for the practical use of the existing scheduling algorithms assuming known interference graphs.

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。

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