智慧城市中按需城市服务的动态时空资源供给
Dynamic Spatio-Temporal Resource Provisioning for On-Demand Urban Services in Smart Cities
AI总结：
本文针对智慧城市中信息不完整下的时空服务请求，提出一种基于递归阈值的动态资源供给方法，结合空间统计与动态最优决策，仿真验证了其优于非策略性方法。
摘要：
在各类按需智慧城市应用中，有限资源的高效分配是一个关键问题。服务请求通常在时间和空间上随机出现，且强度各异。资源供给决策需要实时进行战略性制定，尤其是在未来请求的时间、位置和强度信息不完整的情况下。本文针对集中式源节点向时空服务请求的动态资源供给问题，开发了一种系统化方法。将空间统计与动态最优决策相结合，推导出基于递归阈值的分配策略。所得结果易于在实时应用中计算和实施。为说明框架的有效性，我们给出了常用效用函数的示例，这些函数基于幂律衰减和指数衰减，并结合随机到达的指数分布和均匀分布强度。文中提供了半闭式表达式及递归计算程序。仿真结果表明，与缺乏战略性的方法相比，所提策略具有显著优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
