Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Attiecīgo sensoru datu vākšana× | Mobilās sensora datu vākšana× | |
|---|---|---|
| Nozare | Aptauju metodoloģija | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s–2000s (proliferated with wireless and IoT technologies) | Mid-2000s (smartphone-era formalization ~2006–2010) |
| Autors≠ | Multiple contributors; foundational wireless sensor network (WSN) survey by Akyildiz et al. | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing |
| Tips≠ | Automated quantitative data collection | Passive and active quantitative data collection technique |
| Pirmavots≠ | Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422. DOI ↗ | Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150. DOI ↗ |
| Citi nosaukumi | remote sensing data acquisition, wireless sensor data collection, distributed sensor data collection, telemetric data collection | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Remote sensor data collection is the systematic acquisition of measurements from geographically distributed sensing devices without requiring direct human presence at each location. Sensors continuously or periodically record physical, chemical, or biological variables — temperature, pressure, motion, light, GPS coordinates — and transmit readings wirelessly or via network to a central repository for analysis. Widely used in environmental monitoring, precision agriculture, health informatics, and smart infrastructure. | Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory. |
| ScholarGateDatu kopa ↗ |
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