During the last decade, the mobile application market has grown steadily thanks to the massive use of smartphones and the emergence of cloud computing for offloading computation tasks and improving the quality of experience. With the more recent deployment of Internet of Things (IoT) devices, this cloud-based architectural design and the corresponding communication flow has been maintained. Nevertheless, the increasing amount of information exchanged, the stringent requirements of many IoT applications, and the need for these applications to adapt their behavior in real time to the user's context set these architectural assumptions a challenge. Paradigms such as mobile, mist, and edge computing have recently been proposed to exploit the computational and storage capabilities of current smartphones and IoT devices in order to onload some tasks onto them, reducing the overhead on both the cloud and the network. Currently, the application of these paradigms requires much attention from skilled developers to create ad hoc systems, as there lack standards and tools facilitating their use. This communication introduces Human Microservices as a framework facilitating the deployment of APIs on companion devices in order to provide personal and updated information that can be consumed by other entities. The framework improves the integration of humans in the IoT loop and facilitates the deployment of computation units in devices closer to end users, enhancing system response time by reducing the stress on cloud and network infrastructure. The proposed framework is based on existing standards in order to improve software quality and shorten the learning curve.

中文翻译：

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人类微服务：将人类转变为服务提供者的框架

在过去十年中，由于智能手机的大量使用以及用于卸载计算任务和提高体验质量的云计算的出现，移动应用程序市场稳步增长。随着物联网 (IoT) 设备的最新部署，这种基于云的架构设计和相应的通信流程得到了维护。然而，随着信息交换量的增加、许多物联网应用的严格要求以及这些应用实时调整其行为以适应用户环境的需求，这些架构假设成为一个挑战。范式如移动、雾、最近有人提出边缘计算来利用当前智能手​​机和物联网设备的计算和存储能力，以便将一些任务加载到它们上，从而减少云和网络的开销。目前，这些范式的应用需要熟练的开发人员非常关注来创建临时系统，因为缺乏促进它们使用的标准和工具。这种通信引入了人类微服务作为促进在配套设备上部署 API 的框架，以提供可供其他实体使用的个人和更新信息。该框架改进了人在物联网循环中的集成，并促进了在更接近最终用户的设备中部署计算单元，通过减少云和网络基础设施的压力来提高系统响应时间。提议的框架基于现有标准，以提高软件质量并缩短学习曲线。