Abstract
Wireless Sensor Networks have been proven to have an integral role in the development process of the Internet of Things. The requirements of such networks are increasing rapidly due to the advent of Industry 4.0 and the future internet. The IEEE 802.15.4e-TSCH mode has been designed to satisfy such requirements and ensure guaranteed bandwidth, reliable communication, deterministic latency and energy efficiency. However, scheduling TSCH communications in the time and frequency dimensions has been left open to researchers since it is tightly coupled with the application requirements. A wide range of TSCH schedulers has been proposed in the literature either in a centralized or decentralized fashion. Our work aims at evaluating a selection of decentralized schedulers including distributed, autonomous and Reinforcement-Learning (RL) based ones in a scenario that considers heterogeneous traffic conditions with very high and very low data rates simultaneously. Based on the experiment results, conclusions are drawn for each scheduler to gain insight into their energy efficiency and network performance with respect to applications that must handle highly heterogeneous traffic.
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