The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network saturation and high delays, new architectures such as fog computing are emerging to bring computing infrastructure closer to data sources. Additionally, new data centers are needed to provide real-time Big Data and data analytics capabilities at the edge of the network, where energy efficiency needs to be considered to ensure a sustainable and effective deployment in areas of human activity. In this research, we present an IoT model based on the principles of Model-Based Systems Engineering defined using the Discrete Event System Specification formalism. The provided mathematical formalism covers the description of the entire architecture, from IoT devices to the processing units in edge data centers. Our work includes the location-awareness of user equipment, network, and computing infrastructures to optimize federated resource management in terms of delay and power consumption. We present an effective framework to assist the dimensioning and the dynamic operation of IoT data stream analytics applications, demonstrating our contributions through a driving assistance use case based on real traces and data.

翻译：物联网正在改变我们的社会，提供提升生活质量和资源管理的新服务。这些应用基于由多个分布式设备组成的无处不在的网络，这些设备计算资源和功率有限，能够实时从异构源收集和存储数据。为避免网络饱和和高延迟，雾计算等新架构应运而生，将计算基础设施更靠近数据源。此外，需要在网络边缘部署新的数据中心，以提供实时大数据和数据分析能力，同时必须考虑能效，以确保在人类活动区域实现可持续和有效的部署。在本研究中，我们提出了一种基于模型系统工程原则的物联网模型，该模型使用离散事件系统规范形式化定义。所提供的数学形式化涵盖从物联网设备到边缘数据中心处理单元的整个架构描述。我们的工作包括用户设备、网络和计算基础设施的位置感知，以优化延迟和功耗方面的联邦资源管理。我们提出了一个有效框架，用于辅助物联网数据流分析应用的规模确定和动态运行，并通过基于真实轨迹和数据的驾驶辅助用例展示了我们的贡献。