Resource Provisioning to Spatio‐Temporal Urban Services

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 magnitude. Resource provisioning decisions need to be made strategically in real‐time, particularly when there is incomplete information about the time, location, and magnitude of future requests. In this chapter, 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 magnitude of stochastic arrivals to demonstrate the efficacy of the developed framework. Semi‐closed form expressions along with recursive computational procedure have been provided. Simulation results demonstrate the effectiveness of the proposed policies in comparison with less strategic methodologies.