An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm
An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm
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DOI：
10.1016/j.future.2020.08.001
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Khaled Alanezi;Shivakant Mishra
中科院分区：
文献类型：
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作者：
Khaled Alanezi;Shivakant Mishra
在IoP环境中，边缘计算已经被提出来解决诸如智能手机的边缘设备的资源限制以及云计算平台解决方案所引起的高延迟、用户隐私暴露和网络瓶颈的问题。本文提出了一个上下文管理框架，包括传感器，移动的设备，如智能手机和边缘服务器，使高性能，上下文感知计算的边缘。该架构的主要功能包括为客户端高效地发现可用的传感器和边缘服务，在边缘服务器上进行任务规划和执行的自动化机制，以及可以向框架添加新传感器和服务的动态环境。该架构的原型已经实现，并提出了一个实验评估使用两个计算机视觉任务作为示例服务。性能测量表明，示例任务的执行表现相当不错，所提出的框架非常适合边缘计算环境。
In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment.