  Emerging applications in Internet of Things (IoT), such as remote monitoring
and control, extensively rely on timely status updates. Age of Information
(AoI) has been proposed to characterize the freshness of information in status
update systems. However, it only considers the time elapsed since the
generation of the latest packet, and is incapable of capturing other critical
information in remote control systems, such as the stochastic evolution and the
importance of the source status. In order to evaluate the timeliness of status
updates in remote control systems, we propose a context-aware metric, namely
the context-aware information lapse. The context-aware information lapse
characterizes both the stochastic evolution of the source status and the
context-aware importance of the status. In this paper, the minimization of
average context-aware lapse in a multi-user system is considered, and a
corresponding user scheduling policy is proposed based on Lyapunov
optimization. Numerical results show that compared to AoI-based policy, the
context-aware-lapse-based policy can achieve a substantial improvement in terms
of error-threshold violation probability and control performance.
