  In cyber-physical systems such as automobiles, measurement data from sensor
nodes should be delivered to other consumer nodes such as actuators in a
regular fashion. But, in practical systems over unreliable media such as
wireless, it is a significant challenge to guarantee small enough
inter-delivery times for different clients with heterogeneous channel
conditions and inter-delivery requirements. In this paper, we design scheduling
policies aiming at satisfying the inter-delivery requirements of such clients.
We formulate the problem as a risk-sensitive Markov Decision Process (MDP).
Although the resulting problem involves an infinite state space, we first prove
that there is an equivalent MDP involving only a finite number of states. Then
we prove the existence of a stationary optimal policy and establish an
algorithm to compute it in a finite number of steps.
  However, the bane of this and many similar problems is the resulting
complexity, and, in an attempt to make fundamental progress, we further propose
a new high reliability asymptotic approach. In essence, this approach considers
the scenario when the channel failure probabilities for different clients are
of the same order, and asymptotically approach zero. We thus proceed to
determine the asymptotically optimal policy: in a two-client scenario, we show
that the asymptotically optimal policy is a "modified least time-to-go" policy,
which is intuitively appealing and easily implementable; in the general
multi-client scenario, we are led to an SN policy, and we develop an algorithm
of low computational complexity to obtain it. Simulation results show that the
resulting policies perform well even in the pre-asymptotic regime with moderate
failure probabilities.
