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首页> 外文期刊>Computer Communications >An energy-efficient topology control algorithm for optimizing the lifetime of wireless ad-hoc IoT networks in 5G and B5G
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An energy-efficient topology control algorithm for optimizing the lifetime of wireless ad-hoc IoT networks in 5G and B5G

机译：一种节能拓扑控制算法，用于优化5G和B5G的无线ad-hoc IoT网络寿命

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摘要

Wireless ad-hoc IoT (WAIoT) is promising in providing connections for a considerable amount of devices in the next generation (5G and beyond 5G) networks. A challenge in WAIoT networks is that most network nodes are not stable due to the limited power supply (such as a battery). In this paper, we focus on balancing node residual energy and node degree to prolong the network lifetime. We first present a statistic-based algorithm (named ED-index) for evaluating the network topology and further develop an energy-efficient topology control algorithm (named EDTC). The EDTC algorithm leverages the maximum spanning tree algorithm to build a robust backbone topology and utilizes the proposed ED-index algorithm to re-introduce some edges to the topology. We also present a graph convolutional network (GCN) based algorithm to imitate the initial EDTC algorithm through learning. In the random communication experiment, the proposed EDTC algorithm achieves two times the network lifetime than the state-of-the-art. Moreover, the GCN-based EDTC algorithm saves around 99% optimization time than the initial EDTC algorithm when the number of network nodes is 100.
机译：无线ad-hoc物联网（Waiot）在下一代（5G及超过5G）网络中提供相当数量的设备的连接。 WAIOT网络中的挑战是，由于电源有限（例如电池），大多数网络节点都不稳定。在本文中，我们专注于平衡节点剩余能量和节点度以延长网络寿命。我们首先介绍一种基于统计的算法（命名为ED索引），用于评估网络拓扑，并进一步开发节能拓扑控制算法（名为EDTC）。 EDTC算法利用最大的生成树算法来构建强大的骨干拓扑，并利用所提出的ED索引算法将一些边介绍给拓扑。我们还通过学习呈现基于图形卷积网络（GCN）算法来模仿初始EDTC算法。在随机通信实验中，所提出的EDTC算法达到了比现有技术的网络寿命的两倍。此外，当网络节点的数量为100时，基于GCN的EDTC算法比初始EDTC算法节省了大约99％的优化时间。

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