Adaptive multi-model monitoring of recurrent mobility patterns

Abstract

Multi-model event-triggering is a highly promising technique for efficient monitoring of processes where instead of continuous or even periodic triggering of events, communication and control is only applied after some event interrupt. In this work we investigate an adaptive multi-model monitoring technique whereby a local host that switches between the observed models informs remote hosts of these events which in turn adapt their predictions to reduce prediction error and minimize unnecessary triggering events and future model switching, thereby reducing energy consumption and communication bandwidth. The adaptive technique is examined under a real public transport bus service scenario, where local and remote hosts use a set of mobility models to track travel times and update their arrival schedules according to detected deviations, i.e., event interrupts.

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

“…As detailed in Section I, the focus of this work is on the development of a novel multi-model event triggering (MMET) technique and its performance advantages as compared to the SMET and periodic triggering approaches, as by representing the system state with multiple models the system dynamics are more accurately captured, and at the same time the number of resynchronization events is decreased. Additional work on MMET was also presented in our previous work in [40], where, contrary to this work, we considered the case where vehicles exchange information in order to ascertain their next operating models.…”