Smart farming is a recent innovation in the agriculture sector that can improve the agricultural yield by using smarter, automated, and data driven farm processes that interact with IoT devices deployed on farms. A cloud-fog infrastructure provides an effective platform to execute IoT applications. While fog computing satisfies the real-time processing need of delay-sensitive IoT services by bringing virtualized services closer to the IoT devices, cloud computing provides allows execution of applications with higher computational requirements. The deployment of IoT applications is a critical challenge as cloud and fog nodes vary in terms of their resource availability and use different cost models. Moreover, diverse resource, quality of service (QoS) and security requirements of IoT applications make the problem even more complex. In this paper, we model IoT application placement as an optimization problem that aims at minimizing the resource cost while satisfying the QoS and security constraints. The problem is formulated using Integer Linear Programming (ILP). The ILP model is evaluated for a small-scale scenario.
翻译:智能农业是农业部门最近的一项创新,它可以通过使用智能、自动化和数据驱动的农业流程来提高农业产量,这些流程与农场上安装的IoT装置发生互动。云雾泡沫基础设施提供了执行IoT应用程序的有效平台。雾计算满足了实时处理对延迟敏感的IoT服务的需求,使虚拟化的服务更接近IoT装置,云计算提供了执行计算要求更高的应用软件的机会。部署IoT应用程序是一项重大挑战,因为云雾节点在资源可用性方面各不相同,并且使用不同的成本模型。此外,多种资源、服务质量(QOS)以及IoT应用程序的安全要求使得问题更加复杂。在本文件中,我们将IoT应用程序设置模型作为优化问题,目的是在满足QoS和安全限制的同时最大限度地降低资源成本。这个问题是用Integer Linear 编程(ILP) 设计的。根据小规模的情景对ILP模型进行评估。