In this work, we propose a new framework for blind wireless network topology inference and present a novel solution based on machine learning (ML) techniques. In particular, we seek to identify a causal relationship between the patterns of the radio-frequency (RF) transmissions of the nodes in the network from over-the-air signals observed by a cloud of sensors randomly deployed in the network landscape. The proposed framework is based on simple RF sensors that measure the received power at a rate sufficient to extract traffic patterns. Numerical results based on simulated data show how, despite the propagation impairments and noise may affect the performance of the algorithms, the neural network (NN)-based solution reaches 93% of accuracy even with a relatively low number of sensors.

Machine Learning for Wireless Network Topology Inference

Enrico Testi;Elia Favarelli;Lorenzo Pucci;Andrea Giorgetti
2019

Abstract

In this work, we propose a new framework for blind wireless network topology inference and present a novel solution based on machine learning (ML) techniques. In particular, we seek to identify a causal relationship between the patterns of the radio-frequency (RF) transmissions of the nodes in the network from over-the-air signals observed by a cloud of sensors randomly deployed in the network landscape. The proposed framework is based on simple RF sensors that measure the received power at a rate sufficient to extract traffic patterns. Numerical results based on simulated data show how, despite the propagation impairments and noise may affect the performance of the algorithms, the neural network (NN)-based solution reaches 93% of accuracy even with a relatively low number of sensors.
Proc. of the International Conference on Signal Processing and Communication Systems (ICSPCS)
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Enrico Testi, Elia Favarelli, Lorenzo Pucci, Andrea Giorgetti
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/731911
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