The availability of environmental monitoring data collected by Internet of Things networks can be essential for many critical processes, such as relief operations in disaster areas. The underlying communications infrastructure can be however severely compromised in these scenarios and therefore opportunistic approaches might be needed. Approaches based on information centric networks (ICN), where moving devices forward collected data, have been proposed for opportunistic scenarios but to date, the dynamics of the delivery process in ICNs remain poorly understood. In this paper, we build a family of Markovian models for the delivery process of ICNs in opportunistic scenarios, that allow us to derive the end-to-end delay distribution and the storage ratio in terms of the encounter rate of the moving devices. Furthermore, we investigate how prefetching mechanisms affect the delivery process compared to conventional ICNs. The proposed models are fully validated in a computer simulation environment and demonstrate that the utility of delivery with prefetching reaches its peak in a short time and then decreases at a high rate. Our Markovian models can provide both the insight and quantitative estimations that are needed to design practical ICNs in opportunistic scenarios.

中文翻译：

---

机会场景下以信息为中心的物联网网络分析模型

物联网网络收集的环境监测数据的可用性对于许多关键过程至关重要，例如在灾区的救援行动。但是，在这些情况下，基础通信基础结构可能会受到严重损害，因此可能需要机会主义的方法。已经提出了基于信息中心网络（ICN）的方法，其中移动设备转发收集的数据，用于机会性方案，但是迄今为止，对ICN中传递过程的动态性仍然知之甚少。在本文中，我们为机会情景中的ICN的传递过程建立了马尔可夫模型族，这使我们能够根据移动设备的遇到率来推导端到端的延迟分布和存储率。此外，与传统的ICN相比，我们研究了预取机制如何影响传递过程。所提出的模型在计算机仿真环境中得到了充分验证，并证明了通过预取进行传递的实用性在短时间内达到了顶峰，然后以很高的速度下降。我们的马尔可夫模型可以提供机会主义场景中设计实际ICN所需的洞察力和定量估计。