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 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, diversity in 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 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. The evaluation shows the impact of QoS and security requirement on the cost. We also study the impact of relaxing security constraint on the placement decision.
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提高农业产量,与农场上部署的IOT装置发生互动。云雾泡沫基础设施提供了执行IOT应用的有效平台。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,云计算可以执行计算要求更高的应用程序。部署IOT应用程序是一项重大挑战,因为云和雾节点在资源可得性方面各不相同,并且使用不同的成本模型。此外,资源、服务质量和安全要求的多样性使问题更加复杂。在本文件中,我们将IOT应用定位模型作为优化问题,目的是在满足QOS和安全限制的同时最大限度地降低成本。这个问题是用Integer线性程序(ILP)来设计的。ILP模型用于评估小规模的情景。评估显示了QOS和安全要求对成本的影响。我们还研究了放松安全限制对降低安全限制决定的影响。