Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2491266.2491277 | MCC@SIGCOMM |
Keywords | Field | DocType |
mobile smartphones,unpredictable stress,energy-constrained smartphones,large crowd,traditional crowd-sourcing solution,traditional crowd-sourcing framework,energy-efficient crowd-sourcing framework,in-network crowd-sourcing,current solution,wi-fi radio,crowd behavior,energy management,crowdsourcing | Crowds,Energy management,Computer science,Crowdsourcing,Computer network,Phone,Hierarchy,Bluetooth,Crowd psychology,Scalability,Distributed computing | Conference |
Citations | PageRank | References |
6 | 0.90 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robin Kravets | 1 | 1685 | 174.17 |
| Hilfi Alkaff | 2 | 8 | 1.29 |
| Andrew T. Campbell | 3 | 8958 | 759.66 |
| Karrie Karahalios | 4 | 1674 | 174.11 |
| Klara Nahrstedt | 5 | 7941 | 636.63 |