Time-parameterized sensing task model for real-time tracking

This paper proposes a novel task model in which its physical and temporal parameters are specified as time-parameterized functions and their values are finally determined at the actual dispatch time. This model is clearly differentiated from the classical task model where parameters are fixed at the job release time. The new model better suits sensing tasks in tracking applications, since the sensor parameters such as field-of-view and measurement duration can be properly adjusted at the actual sensing time. The new model, however, creates the cyclic dependency between task parameters and scheduling behavior, that is, the task parameters depend on scheduling behavior and the latter in turn depends on the former. This cyclic dependency makes the schedulability check even more difficult. We handle this difficulty by iterative convergence and probabilistic schedulability envelope, which provides an efficient online schedulability check. The experimental study shows that the new model significantly improves the effective capacity of tracking systems without losing track accuracy.