MAIDE:增强现实(AR)便利的移动系统,用于轻松登录物联网(IoT)设备

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Huanle Zhang, M. Uddin, F. Hao, S. Mukherjee, P. Mohapatra
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引用次数: 1

摘要

高效的入职流程是利用和配置物联网设备访问网络基础设施的关键一步。然而,目前板载物联网设备的过程既耗时又费力,这使得该过程容易受到人为错误和安全风险的影响。为了简化入职流程,我们需要一种机制来可靠地将每个数字身份与每个物理设备关联起来。我们设计了一种名为MAIDE的入职机制来填补这一技术空白。MAIDE是一个增强现实(AR)便利的应用程序,系统地选择多个测量位置,计算每个位置的测量时间,并指导用户完成测量过程。该应用程序还使用优化的基于投票的算法,根据测量数据导出设备到id的映射。这种方法不需要对现有的物联网设备或基础设施进行任何修改,可以应用于所有主要的无线协议,如BLE和WiFi。大量实验表明,MAIDE实现了较高的设备到id映射精度。例如,在典型的企业环境中,为了区分天花板上的两个设备,MAIDE在设备相距4英尺时,通过测量每个测量位置的5秒接收信号强度(RSS)数据,达到了~95%的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MAIDE: Augmented Reality (AR)-facilitated Mobile System for Onboarding of Internet of Things (IoT) Devices at Ease
Having an efficient onboarding process is a pivotal step to utilize and provision the IoT devices for accessing the network infrastructure. However, the current process to onboard IoT devices is time-consuming and labor-intensive, which makes the process vulnerable to human errors and security risks. In order to have a streamlined onboarding process, we need a mechanism to reliably associate each digital identity with each physical device. We design an onboarding mechanism called MAIDE to fill this technical gap. MAIDE is an Augmented Reality (AR)-facilitated app that systematically selects multiple measurement locations, calculates measurement time for each location and guides the user through the measurement process. The app also uses an optimized voting-based algorithm to derive the device-to-ID mapping based on measurement data. This method does not require any modification to existing IoT devices or the infrastructure and can be applied to all major wireless protocols such as BLE, and WiFi. Our extensive experiments show that MAIDE achieves high device-to-ID mapping accuracy. For example, to distinguish two devices on a ceiling in a typical enterprise environment, MAIDE achieves ~95% accuracy by measuring 5 seconds of Received Signal Strength (RSS) data for each measurement location when the devices are 4 feet apart.
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
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