In this paper, we propose a novel context-aware routing (CAR) approach that uses the cloud as an extra level of data-request processing to improve the network performance in terms of data delivery. Data delivery in the Internet of Things depends heavily on numerous factors, such as the amount of data, end-to-end in-network delay, and setup time. The CAR approach is significantly improving the current request-response model, especially while the exchanged in-network data amount increases and data are sent from source to destination in a peer-to-peer fashion. What we are trying to show in this paper, in particular, is the benefits of having a central context-aware server (in the cloud) in improving the end-user experience. Hence, the proposed CAR approach is a typical candidate for data-intensive cloud-based applications. It considers source and destination requirements in terms of data size, delay, link capacity, and available applications on the operating devices as well. Extensive simulations are performed, and achieved results show the efficiency of our approach against other competitive approaches in terms of in-network delay and packet delivery ratio.

中文翻译：

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用于物联网的 CAR 方法

在本文中，我们提出了一种新颖的上下文感知路由 (CAR) 方法，该方法将云用作数据请求处理的额外级别，以提高数据交付方面的网络性能。物联网中的数据传输在很大程度上取决于许多因素，例如数据量、端到端网络延迟和设置时间。CAR 方法显着改进了当前的请求-响应模型，特别是当交换的网络内数据量增加并且数据以对等方式从源发送到目的地时。我们试图在本文中特别展示的是拥有中央上下文感知服务器（在云中）在改善最终用户体验方面的好处。因此，所提出的 CAR 方法是数据密集型基于云的应用程序的典型候选者。它考虑了数据大小、延迟、链路容量和操作设备上的可用应用程序方面的源和目标要求。进行了广泛的模拟，取得的结果表明我们的方法在网络延迟和数据包传输率方面与其他竞争方法相比的效率。