  This paper investigates the problem of sensor scheduling for remotely
estimating the states of heterogeneous dynamical systems over resource-limited
and lossy wireless channels. Considering the low time complexity and high
versatility requirements of schedulers deployed on the transport layer, we
propose a lightweight scheduler based on an Age of Information (AoI) function
built with the tight scalar upper bound of the remote estimation error. We show
that the proposed scheduler is indexable and sub-optimal. We derive an upper
and a lower bound of the proposed scheduler and give stability conditions for
estimation error. Numerical simulations demonstrate that, compared to existing
policies, the proposed scheduler achieves estimation performance very close to
the optimal at a much lower computation time.
