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移动感测系统。

Mobile sensing systems.

机构信息

Grupo de Arquitectura y Concurrencia (GAC), Departamento de Ingeniería Telemática, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria (Gran Canaria) 35017, Spain.

出版信息

Sensors (Basel). 2013 Dec 16;13(12):17292-321. doi: 10.3390/s131217292.

Abstract

Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular… Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.

摘要

富传感器智能手机的出现使得移动感应研究领域成为无处不在感应的一部分,它整合了无线传感器网络和网络感应等其他领域。移动感应有几种类型:个体感应、参与式感应、机会感应、群体感应、社会感应等。感应的对象可以是以人为主或以环境为主。感应的领域可以是家庭、城市、车辆等。目前,一些障碍限制了移动感应系统的社会接受度。社会障碍的例子包括隐私问题、一些国家的限制性法律以及缺乏经济激励措施,这些因素可能会阻碍人们参与感应活动。一些技术障碍包括手机节能以及管理传感器和软件的多样性。一些现有的调查部分涉及移动感应系统的主题。已发表的论文从理论上或部分解决了上述障碍。我们通过新的工作来补充上述调查,审查移动感应系统的障碍,并为任何类型的移动感应系统提出有效实施感应、融合、学习、安全、隐私和节能的思路,并提出了一些现实的研究挑战。主要目标是降低移动感应系统的学习曲线,因为其复杂性非常高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84ea/3892889/e1e852aa8e1c/sensors-13-17292f1.jpg

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