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Efficient allocation of finite resources is a crucial problem in a widevariety of on-demand smart city applications. Service requests often appearrandomly over time and space with varying intensity. Resource provisioningdecisions need to be made strategically in real-time, particularly when thereis incomplete information about the time, location, and intensity of futurerequests. In this paper, we develop a systematic approach to the dynamicresource provisioning problem at a centralized source node to spatio-temporalservice requests. The spatial statistics are combined with dynamically optimaldecision-making to derive recursive threshold based allocation policies. Thedeveloped results are easy to compute and implement in real-time applications.For illustrative purposes, we present examples of commonly used utilityfunctions, based on the power law decay and exponential decay coupled withexponentially, and uniformly distributed intensity of stochastic arrivals todemonstrate the efficacy of the developed framework. Semi-closed formexpressions along with recursive computational procedure has been provided.Simulation results demonstrate the effectiveness of the proposed policies incomparison with less strategic methodologies.

Dynamic Spatio-Temporal Resource Provisioning for On-Demand Urban Services in Smart Cities