Random Neural Networks with Hierarchical Committees for Improved Routing in Wireless Mesh Networks with Interference

被引：0

作者：

Rataj A. ^{[1
]}

机构：

[1] IITiS PAN, Baltycka 5, Gliwice

来源：

SN Computer Science | 2020年 / 1卷 / 1期

基金：

欧盟地平线“2020”;

关键词：

Cognitive packet routing; Electromagnetic interference; Hierarchical neural network; Random neural network; Real-time routing; Wireless sensor;

D O I：

10.1007/s42979-019-0038-4

中图分类号：

学科分类号：

摘要：

We propose a hierarchical (nested) variant of a recurrent random neural network (RNN) with reinforced learning, introduced by Gelenbe. Each neuron (committee) in a top-level RNN represents a different bottom-level RNN (or sub-committee). The bottom-level RNNs choose the best routing and the top-level RNN chooses the currently best bottom-level RNN. Each of the bottom RNNs is trained in a different way. When they differ in their choice of the best path, several cognitive packets are routed according to the different decisions. In that case, a respective ACK packet trains individual bottom RNNs and not all bottom RNNs at once. An example presents an optimisation of a real-time routing in a dense mesh network of wireless sensors relaying small metering messages between each other, until the messages reach a common gateway. The network is experiencing a periodic electromagnetic interference. The hierarchical variant causes a small increase in the number of smart packets but allows a considerably better routing quality. © 2019, The Author(s).

引用

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