Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks

2025-09-09

IEEE International Conference on Emerging Technologies and Factory Automation

AI Summary

TSCH-based wireless sensor networks face challenges in dynamic energy conservation when operating without pre-established schedules. Method: This paper proposes a machine learning–driven timeslot utilization prediction mechanism. It constructs a high-fidelity node-level simulation model to generate spatiotemporally rich traffic data, designs TSCH-aware feature engineering leveraging the protocol’s slotframe structure, and systematically evaluates the prediction performance degradation of multiple ML models across hierarchical levels of tree-topology networks. Contribution/Results: We introduce a novel hierarchical-adaptive lightweight prediction framework enabling nodes to autonomously enter deep sleep during idle timeslots. Experiments under industrial-scale scenarios demonstrate an average 38.7% reduction in per-node power consumption and a 2.1× improvement in overall network energy efficiency. This work is the first to identify and resolve the critical issue of prediction performance degradation with increasing hop depth in multihop TSCH networks.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• Predicts slot usage in TSCH networks using machine learning
• Enables deep sleep states to enhance energy efficiency
• Analyzes prediction accuracy across different network tree levels

Innovation

• Machine learning predicts TSCH network traffic patterns
• Deep sleep activation during idle periods saves energy
• Models tested on simulated wireless sensor node data