The ParticipAct Mobile Crowd Sensing Living Lab: The Testbed for Smart Cities
The ParticipAct Mobile Crowd Sensing Living Lab: The Testbed for Smart Cities
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DOI：
10.1109/mcom.2014.6917406
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发表时间：
2014-10-01
影响因子：
11.2
通讯作者：
Foschini, Luca
中科院分区：
文献类型：
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作者：
Cardone, Giuseppe;Cirri, Andrea;Foschini, Luca
近年来，配备传感器的智能手机的广泛使用使得利用用户设备在城市地区收集大量数据成为可能，从而实现了所谓的移动人群感知（MCS）。尽管据我们所知，人们已经做出了许多努力来改进 MCS 的具体技术（从信号处理到向用户分配数据收集活动，再到整个数据处理范围），但迄今为止，还没有在大规模现实世界实验中涉及所有这些技术的 MCS 积极实验。基于这些考虑，我们启动了 ParticipAct Living Lab 测试平台，这是博洛尼亚大学正在进行的一项实验，涉及 300 名学生为期一年的人群感知活动，可以被动访问智能手机传感器，也需要主动用户协作。在本文中，我们描述了 ParticipAct 设计背后的准则及其功能和架构。此外，我们报告了部署前三个月收集的一些重要结果，包括 ParticipAct 客户端提供的分类器的准确性以及用户成功完成任务的倾向，具体取决于执行任务所需的积极协作级别。
In recent years, the widespread availability of smartphones provided with sensors has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling so-called mobile crowd sensing (MCS). While many efforts have been made to improve specific techniques for MCS - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing spectrum - to the best of our knowledge, thus far there have been no active experiments of MCS that involve all these techniques in a large-scale real-world experiment. Based on these considerations, we started the ParticipAct Living Lab testbed, an ongoing experiment at the University of Bologna involving 300 students for one year in crowd sensing campaigns that can passively access smartphone sensors and also require active user collaboration. In this article we describe the guidelines behind the design of ParticipAct, as well as its features and architecture. Moreover, we report some of the seminal results gathered during the first three months of its deployment, including accuracy of the classifier provided by ParticipAct client and user inclination to successfully complete tasks depending on the level of active collaboration required to executing them.