Benchmarking the State of Networks with a Low-Cost Method Based on Reservoir Computing

TL;DR

This paper presents a low-cost, non-invasive method using reservoir computing to monitor mobile network states by analyzing aggregated data, demonstrating its effectiveness and potential for real-time network health assessment.

Contribution

It introduces a reservoir computing-based approach for network monitoring that leverages readily available data and requires minimal training, offering an inexpensive and agnostic solution.

Findings

Performance correlates with network state changes

Perturbations reduce model performance

Method is energy-efficient and scalable

Abstract

Using data from mobile network utilization in Norway, we showcase the possibility of monitoring the state of communication and mobility networks with a non-invasive, low-cost method. This method transforms the network data into a model within the framework of reservoir computing and then measures the model's performance on proxy tasks. Experimentally, we show how the performance on these proxies relates to the state of the network. A key advantage of this approach is that it uses readily available data sets and leverages the reservoir computing framework for an inexpensive and largely agnostic method. Data from mobile network utilization is available in an anonymous, aggregated form with multiple snapshots per day. This data can be treated like a weighted network. Reservoir computing allows the use of weighted, but untrained networks as a machine learning tool. The network, initialized as a so-called echo state network (ESN), projects incoming signals into a higher dimensional space, on which a single trained layer operates. This consumes less energy than deep neural networks in which every weight of the network is trained. We use neuroscience inspired tasks and trained our ESN model to solve them. We then show how the performance depends on certain network configurations and also how it visibly decreases when perturbing the network. While this work serves as proof of concept, we believe it can be elevated to be used for near-real-time monitoring as well as the identification of possible weak spots of both mobile communication networks as well as transportation networks.