Model-Adaptive Event Triggering for Monitoring Recurrent Mobility Patterns in Public Transport

Abstract

Event-triggering (ET) is a highly promising technique for efficient operation of Internet of Things (IoT) devices, where instead of continuous or even periodic triggering of events, communication and control is only applied after some event interrupt. In this work, the proposed event-triggering technique is examined and applied on public transportation services, having as an objective to provide good tracking accuracy for a fleet of buses, while limiting communication between the system's components. Specifically, an ET technique is proposed, where a local and a remote host use behavior modeling to track the evolution of the system and synchronization messages are only sent when deviations are detected between the nominal model and the actual behavior of the system. This work focuses on a multi-model event triggering and proposes and develops a multi-model ET (MMET) technique, where multiple models are derived to accurately represent the system state. This is achieved by utilizing data-driven approaches that are used to analyze recurrent patterns and predict the system's behavior. In this way, both local and remote hosts can adapt to system changes by switching to the most accurate model that best represents the underlying system settings. The proposed MMET technique is subsequently compared to the single-model event triggering (SMET) approach, as well as traditional periodic triggering techniques, demonstrating that MMET can provide better performance in terms of tracking accuracy at a lower number of communication events, reducing in this way communication energy consumption as well.