About the relationship between people and discoverable Bluetooth devices in urban environments
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
MobiClique: middleware for mobile social networking
Proceedings of the 2nd ACM workshop on Online social networks
U-connect: a low-latency energy-efficient asynchronous neighbor discovery protocol
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds
Searchlight: helping mobile devices find their neighbors
MobiHeld '11 Proceedings of the 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds
Enabling multiple controllable radios in OMNeT++ nodes
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
QuickSilver: application-driven inter- and intra-cluster communication in Vanets
Proceedings of the third ACM international workshop on Mobile Opportunistic Networks
United we find: enabling mobile devices to cooperate for efficient neighbor discovery
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
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Proliferation of mobile smartphones has opened up possibilities of using crowd-sourcing to gather data from and so monitor large crowds. However, depending on the size of the crowd, current solutions either put unpredictable stress on the infrastructure and energy-constrained smartphones or do not capture the crowd behavior accurately. In response, we present CrowdWatch, a scalable, distributed and energy-efficient crowd-sourcing framework. CrowdWatch achieves its goal through off-loading some of the processing to the devices and establishing a hierarchy of participants by exploiting devices with multiple radios (i.e. WiFi (high-power) and BlueTooth (low-power)). CrowdWatch can outperform traditional crowd-sourcing frameworks by reducing the stress on the infrastructures to 10% of that of a traditional crowd-sourcing solution, while only requiring each phone to use their Wi-Fi radios 15% of the time in a dense environment.