Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience

Antonio Corradi; Antonio Puliafito; Luca Foschini; Paolo Bellavista; Raffaele Ianniello

摘要

Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers’ behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year.

机译：如今，具有丰富传感器功能的智能手机有可能通过吸引市民参与移动虚拟传感器来覆盖智能城市感兴趣的区域，从而在城市环境中收集大量有价值的传感数据。本文提出了一项与挑战性的技术问题有关的深入研究，这些问题与将移动人群感应（MCS）数据收集任务有效分配给人群感应运动中的志愿者有关。特别是，本文最初描述了如何通过包含一些新设施来提高提议的感知活动的有效性，这些新设施包括能够描述和预测用户移动性模式的精确参与者选择算法，游戏技术以及及时的地理通知。报告的结果表明，从志愿者的行为中利用分析趋势/预测技术的可行性;此外，他们基于ParticipAct生活实验室中进行的大规模和真实MCS数据活动，定量比较了不同的MCS任务分配策略，该活动是一项正在进行的MCS现实世界实验，涉及博洛尼亚大学的170多名学生超过一年。

Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience

摘要

著录项

相关主题

期刊订阅