一种可穿戴设备功能模块共享的虚拟本地中心解决方案

Hsin-Peng Lin, Yuan-Yao Shih, Ai-Chun Pang, Yuan-Yao Lou
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引用次数: 5

摘要

可穿戴设备是一种佩戴在人体上的小型电子设备,具有低水平的处理和存储能力,并提供某些类型的集成功能。最近,可穿戴设备越来越受欢迎,各种可穿戴设备在市场上推出;然而,可穿戴设备需要一个强大的本地中心,大多数是智能手机,以补充高级功能的处理和存储能力。有时携带本地集线器(智能手机)可能不方便;因此,许多可穿戴设备都配备了Wi-Fi接口,可以在本地集线器不在附近时通过互联网与本地集线器交换数据。然而,这将导致较长的响应时间和受限的功能。在本文中,我们提出了一个虚拟的本地集线器解决方案,它利用附近的网络设备(例如Wi-Fi ap)作为本地集线器。由于分别迁移所有服务于可穿戴设备的应用会占用过多的网络资源和存储资源,本方案将功能模块部署到多个网络节点,实现不同用户和应用之间的远程功能模块共享。为了减少解决方案对网络带宽的影响,我们提出了一种以最小化总带宽消耗为目标的启发式功能模块分配算法。我们进行了一系列的实验,结果表明,所提出的解决方案可以将带宽消耗减少一半,并且在大量业务请求的情况下仍然可以满足所有请求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Virtual Local-hub Solution with Function Module Sharing for Wearable Devices
Wearable devices, which are small electronic devices worn on a human body, are equipped with low level of processing and storage capacities and offer some types of integrated functionalities. Recently, wearable device is becoming increasingly popular, various kinds of wearable device are launched in the market; however, wearable devices require a powerful local-hub, most are smartphone, to replenish processing and storage capacities for advanced functionalities. Sometime it may be inconvenient to carry the local-hub (smartphone); thus, many wearable devices are equipped with Wi-Fi interface, enabling them to exchange data with local-hub though the Internet when the local-hub is not nearby. However, this results in long response time and restricted functionalities. In this paper, we present a virtual local-hub solution, which utilizes network equipment nearby (e.g., Wi-Fi APs) as the local-hub. Since migrating all applications serving the wearable devices respectively takes too much networking and storage resources, the proposed solution deploys function modules to multiple network nodes and enables remote function module sharing among different users and applications. To reduce the impact of the solution on the network bandwidth, we propose a heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We conduct series of experiments, and the results show that the proposed solution can reduce the bandwidth consumption by up to half and still serve all requests given a large number of service requests.
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