Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LPWANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases.

最近，随着物联网（IoT）的出现，智能城市已成为大多数云服务提供商的潜在商机。但是，集中式云架构无法满足许多物联网服务所施加的要求。高移动性覆盖范围和低延迟约束是最严格的要求，这使得集中式解决方案不切实际。作为响应，已经引入了雾计算的理论基础，以通过将计算资源放置在最终用户附近来建立分布式云基础架构。但是，对其基本概念的接受仍处于早期阶段。仍需解决的关键挑战是雾计算中的服务功能链（SFC），其中服务以特定顺序连接，形成一条服务链，以充分利用网络软化。也，低功耗广域网（LPWAN）受到了广泛关注。与传统的无线技术相反，LPWAN专注于长距离的低带宽通信。尽管它们具有巨大的潜力，但在这些技术的部署和管理方面仍然存在许多挑战，这使得大多数服务提供商难以广泛采用它们。本文提出了一种针对IoT服务分配问题的混合整数线性规划（MILP）公式，该公式考虑了SFC概念，不同的LPWAN技术和多个优化目标。据我们所知，我们的工作超出了当前的最新水平，在考虑云和无线网络需求的同时，在雾云环境中提供了完整的端到端（E2E）资源配置。已经进行了评估，以详细评估针对智慧城市用例的拟议MILP公式。结果显示了不同配置策略之间的明确权衡。我们的工作可以用作雾云环境中资源调配研究的基准，因为模型方法是通用的，可以应用于各种物联网用例。