A cluster-based approach to cooperative mobile crowd monitoring

了解人群出现的位置对于许多应用程序至关重要，例如，有效管理应急响应人员、志愿者或公共交通等资源。移动电话感应可能是一种廉价而灵活的获取实时人群信息的方式，因为它允许社区用他们的移动电话收集传感器数据，否则需要部署专用的传感基础设施。然而，个人频繁将传感器数据上传到远程服务器的非合作方法会使网络基础设施和参与手机的电池紧张。这会导致挫败感、参与度降低和信息准确性降低。点对点和无线传感器网络的研究已经通过设备对设备通信、分散节点集群、工作轮换、占空循环和分布式感知等合作概念解决了流量卸载和节能问题。然而，目前尚不清楚的是：1)这些概念应用于移动人群监测时是否合适，2)这些概念如何在手机上相互整合，以及3)它们共同应用时会产生什么影响。协作式移动电话传感技术，即个人网络和捆绑他们的数据，才刚刚开始出现，目前的方法只能部分解决这些研究问题。特别是，现有的解决方案是为小规模合作而设计的，尚未适应群体的动态，或者没有同时研究多个概念的影响。该项目将通过反复分析不同的集群和节能技术在移动电话、大规模模拟自组织网络和真实人群场景中的成本和收益来调查研究问题。它将开发Conic，一种节能和自适应的集群方案，用于合作移动人群监测。有了最终的原型和定量数据，该项目希望有助于更好地理解如何利用能量受限的移动电话瞬态网络来监测一群人。此外，它希望为研究人员和实践者提供证据，以支持他们对移动传感应用的可行方法的决定。从长远来看，该项目将为进一步研究基于人群的传感及其与智慧城市概念的融合奠定基础。

Knowing where crowds of people emerge is essential for many applications, e.g., to efficiently manage resources like emergency response personnel, volunteers, or public transport. Mobile phone sensing may be an inexpensive and flexible way to obtain real-time crowd information as it lets the community collect sensor data with their mobile phones where otherwise dedicated sensing infrastructure needs to be deployed. However, non-cooperative approaches where individuals frequently upload sensor data to a distant server strain the network infrastructure and the battery of the participating phones. This leads to frustration, less participation, and reduced information accuracy.Research on peer-to-peer and wireless sensor networks has addressed traffic off-loading and energy conservation with cooperative concepts like device-to-device communication, decentralised node clustering, job rotation, duty cycling, and distributed sensing. It is, however, unclear: 1) how suitable these concepts are when applied to mobile crowd monitoring, 2) how these concepts integrate with each other on mobile phones, and 3) what their impact is when collectively applied. Cooperative mobile phone sensing, where individuals network and bundle their data, has only started to emerge and current approaches addresses these research questions only partially. In particular, existing solutions are designed for small-scale cooperation, are not yet adaptive to the dynamics of the crowd, or do not study the effect of multiple concepts simultaneously. This project will investigate the research questions by iteratively analysing the costs and benefits of different clustering and energy conservation techniques on mobile phones, large-scale simulated ad hoc networks, and in real crowd scenes. It will develop Conic, an energy-efficient and adaptive clustering scheme for cooperative mobile crowd monitoring. With the resulting prototype and quantitative data, the project hopes to contribute to a better understanding of how a crowd of people can be monitored with a transient network of energy-constrained mobile phones. Further, it hopes to provide researchers and practitioners with evidence to support their decision on a viable approach for mobile sensing applications. In the long run, the project will establish the basis for further research in crowd-based sensing and its integration into smart city concepts.