Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks

TL;DR

This paper explores machine learning techniques to predict traffic patterns in TSCH wireless sensor networks, enabling nodes to sleep when idle and significantly reducing power consumption while maintaining network reliability.

Contribution

It introduces a machine learning approach to predict TSCH network traffic, optimizing energy efficiency by enabling deep sleep modes based on traffic forecasts.

Findings

ML models effectively predict traffic at various network levels

Prediction accuracy decreases near the root of the network tree

Simulated data shows substantial energy savings using ML predictions

Abstract

Wireless sensor networks (WSNs) are employed across a wide range of industrial applications where ultra-low power consumption is a critical prerequisite. At the same time, these systems must maintain a certain level of determinism to ensure reliable and predictable operation. In this view, time slotted channel hopping (TSCH) is a communication technology that meets both conditions, making it an attractive option for its usage in industrial WSNs. This work proposes the use of machine learning to learn the traffic pattern generated in networks based on the TSCH protocol, in order to turn nodes into a deep sleep state when no transmission is planned and thus to improve the energy efficiency of the WSN. The ability of machine learning models to make good predictions at different network levels in a typical tree network topology was analyzed in depth, showing how their capabilities degrade while approaching the root of the tree. The application of these models on simulated data based on an accurate modeling of wireless sensor nodes indicates that the investigated algorithms can be suitably used to further and substantially reduce the power consumption of a TSCH network.