A cluster-based approach to cooperative mobile crowd monitoring

• Applicant: Dr. Christin Groba
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: from 2015 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 275521334

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.

• DFG Programme: Research Grants
• International Connection: Ireland, Portugal
• Cooperation Partners: Professor Dr. Jorge Cardoso; Professorin Dr. Siobhán Clarke; Dr.-Ing. Thomas Springer