Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks

TL;DR

The paper tackles energy efficiency in industrial TSCH wireless sensor networks by predicting per-link slot usage with an ML model. It frames slot usage as a time-series prediction problem and uses a two-layer MLP to forecast future transmissions across a 4-layer tree topology, measuring predictability via autocorrelation and standard classification metrics. Results show high predictive performance for leaf-level links and substantial reductions in idle listening energy, with performance gradually degrading toward the root. The work demonstrates a viable path to energy savings in deploy-and-forget WSNs while highlighting integration challenges and areas for future exploration, such as accounting for ML computation energy and broader topologies.

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.

Figures (4)

• Figure 1: Example of a TSCH slotframe matrix.
• Figure 2: Example network topology that shows a routing and the corresponding representation.
• Figure 3: Topology of the network considered in the experimental analysis.
• Figure 4: The evolution of key quality indicators as levels change.