Content-centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content-centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache-enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell-partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

中文翻译：

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基于内容中心网络中节点空间分布的扩展性能分析与优化

以内容为中心的网络（CCN）已成为缓解无线流量需求增长的有前途的技术。在本文中，我们基于节点的非均匀空间分布来探索以移动内容为中心的网络的扩展性能，其中每个节点围绕其自身的原点移动，并根据Zipf分布请求所需的内容。我们假设每个移动节点都配备了有限的本地缓存，该缓存将按照静态缓存分配方案应用于缓存内容。根据启用缓存的节点的空间分布不均匀，我们引入了两种聚类模型，即聚类网格模型和聚类随机模型。在每个群集模型中，我们通过提出的小区划分调度方案和分布式多跳路由方案，分析了当节点数变为无穷大时的吞吐量和延迟性能。我们表明，节点移动度和聚类行为在上述渐近性能中起着基本作用。最后，我们研究了两种聚类模型中的最佳缓存分配问题。我们的发现为开发最佳缓存方案提供了指导。我们进一步执行数值模拟以验证理论比例定律。我们研究了两种聚类模型中的最佳缓存分配问题。我们的发现为开发最佳缓存方案提供了指导。我们进一步执行数值模拟以验证理论比例定律。我们研究了两种聚类模型中的最佳缓存分配问题。我们的发现为开发最佳缓存方案提供了指导。我们进一步执行数值模拟以验证理论比例定律。