Many applications operating in the Internet of Things (IoT) require timely and fair data collection from devices. This has motivated research into a new metric called Age of Information (AoI). This paper contributes to this effort by proposing to minimize the maximum average AoI (min-max AoI) in a multi-hop IoT network comprising of solar-powered Power Beacons (PBs). It outlines a Mixed Integer Linear Program (MILP) that jointly optimizes: (i) the beamforming vector used by PBs to charge devices, and (ii) routing, which determines how samples from devices are forwarded to a sink node, and (iii) the sampling time of sources. It also presents two protocols: Centralized Linear Relaxation (CLR) and Distributed Path Selection (DPS), respectively. CLR is run by the sink to determine the transmit power of PBs and the path of each source using two Linear Programs (LPs). On the other hand, DPS is a distributed approach whereby PBs and sources make their own decisions using local information. Our simulation results show that min-max AoI increases with the number of sources, but reduces with increasing number of PBs. The number of paths available to a source, the number of frames, and solar panel size have limited impact on performance. The min-max AoI of CLR and DPS is $1.60\times $ and $1.95\times $ higher than that of MILP.

中文翻译：

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优化射频供电的多跳无线网络中的信息新鲜度

许多在物联网 (IoT) 中运行的应用程序需要从设备中及时、公平地收集数据。这激发了对称为信息时代 (AoI) 的新指标的研究。本文通过建议最小化由太阳能供电信标 (PB) 组成的多跳物联网网络中的最大平均 AoI (min-max AoI) 来促进这项工作。它概述了一个混合整数线性程序 (MILP)，它联合优化：(i) PB 用于为设备充电的波束成形矢量，以及 (ii) 路由，它决定如何将来自设备的样本转发到接收节点，以及 (iii)源的采样时间。它还提出了两种协议：分别是集中线性松弛 (CLR) 和分布式路径选择 (DPS)。CLR 由宿运行以确定 PB 的发射功率和使用两个线性规划 (LP) 的每个源的路径。另一方面，DPS 是一种分布式方法，PB 和来源利用本地信息做出自己的决定。我们的模拟结果表明，最小-最大 AoI 随着源数量的增加而增加，但随着 PB 数量的增加而减少。可用于源的路径数、帧数和太阳能电池板尺寸对性能的影响有限。CLR 和 DPS 的最小-最大 AoI 为 和太阳能电池板尺寸对性能的影响有限。CLR 和 DPS 的最小-最大 AoI 为 和太阳能电池板尺寸对性能的影响有限。CLR 和 DPS 的最小-最大 AoI 为 $1.60\乘以 $和 1.95 美元\倍高于 MILP。