Research map / Large language models / AI and society
An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Best-Match (clustered, look-ahead stochastic coordination) instead of Naive Greedy | participatory sensing coordination · Beijing PM2.5, 250 participants, time-scale 1 | total utility: 75.34% better | Helped |
| use Best-Match instead of Naive Greedy | participatory sensing coordination · Beijing PM2.5, 500 participants, time-scale 1 | total utility: 49.13% better | Helped |
| use Best-Match instead of Naive Greedy | participatory sensing coordination · Beijing PM2.5, 750 participants, time-scale 1 | total utility: 12.71% better | Helped |
| use Best-Match instead of simple algorithms when phenomenon is less dynamic (time-scale above 3) | participatory sensing coordination · Beijing PM2.5, varying time-scale | total utility | Hurt |
| use Best-Match instead of Naive Greedy for runtime | participatory sensing coordination · Beijing PM2.5, runtime | runtime | Helped |
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