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Enabling Efficient Communications with Resource Constrained Information Endpoints in Smart Homes

机译:在资源有限的智能家居中通过资源受限的信息端点实现高效通信

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摘要

Smart Homes are one of the most promising real applications of Internet of Things and Cyber-Physical Systems. Devices and software components are deployed to create enhanced living environments where physical information is captured by sensors, sent to servers and finally transmitted to information endpoints to be consumed after its processing. These systems usually employ resource constrained components in dense architectures supported by massive machine type communications. Components, to adapt to different scenarios, present several configuration options. In machine type communications, these configuration options should be selected dynamically and automatically. Many works have addressed this situation in relation to sensor-server communications but endpoints are still mostly manually configured. Therefore, in this paper it is proposed an automatic and dynamic configuration algorithm, based on the idea of “efficiency,” for information endpoints in the context of Smart Homes. Different costs associated to endpoint-server communications in Smart Homes are identified and mathematically modelled. Using this model and real measurements, the most efficient configuration is selected for each endpoint at each moment, not only guarantying the interoperability of devices but also ensuring an adequate resource usage, for example, modifying the endpoints’ lifecycle or the information compression mechanism. In order to validate the proposed solution, an experimental validation including both real implementation and simulation scenarios is provided.
机译:智能家居是物联网和网络物理系统最有前途的实际应用之一。部署设备和软件组件以创建增强的生活环境,在该生活环境中,物理信息由传感器捕获,发送到服务器,最后传输到信息端点,以便在处理后消耗。这些系统通常在大规模机器类型通信支持的密集架构中采用资源受限的组件。为了适应不同情况,组件提供了几种配置选项。在机器类型的通信中,应动态自动选择这些配置选项。许多工作已经解决了与传感器-服务器通信有关的这种情况,但是端点仍然大多是手动配置的。因此,本文提出了一种基于“效率”的自动动态配置算法,用于智能家居环境中的信息端点。确定并数学建模了与智能家居中的端点服务器通信相关的不同成本。使用此模型和实际测量结果,可以在每个时刻为每个端点选择最有效的配置,不仅可以确保设备的互操作性,还可以确保适当的资源使用,例如,修改端点的生命周期或信息压缩机制。为了验证所提出的解决方案,提供了包括实际实现和仿真方案的实验验证。

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