  Efficient allocation of finite resources is a crucial problem in a wide
variety of on-demand smart city applications. Service requests often appear
randomly over time and space with varying intensity. Resource provisioning
decisions need to be made strategically in real-time, particularly when there
is incomplete information about the time, location, and intensity of future
requests. In this paper, we develop a systematic approach to the dynamic
resource provisioning problem at a centralized source node to spatio-temporal
service requests. The spatial statistics are combined with dynamically optimal
decision-making to derive recursive threshold based allocation policies. The
developed results are easy to compute and implement in real-time applications.
For illustrative purposes, we present examples of commonly used utility
functions, based on the power law decay and exponential decay coupled with
exponentially, and uniformly distributed intensity of stochastic arrivals to
demonstrate the efficacy of the developed framework. Semi-closed form
expressions along with recursive computational procedure has been provided.
Simulation results demonstrate the effectiveness of the proposed policies in
comparison with less strategic methodologies.
