Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Innsamling av sensordata på nett× | Innsamling av mobile sensordata× | |
|---|---|---|
| Fagfelt | Surveymetodikk | Surveymetodikk |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000) | Mid-2000s (smartphone-era formalization ~2006–2010) |
| Opphavsperson≠ | Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000) | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing |
| Type≠ | Quantitative / mixed-mode data collection technique | Passive and active quantitative data collection technique |
| Opprinnelig kilde≠ | 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 ↗ |
| Alias | networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection |
| Relaterte≠ | 6 | 4 |
| Sammendrag≠ | Online sensor data collection is a systematic technique for gathering continuous or event-triggered measurements from physical sensors that transmit readings in real time over a network — the internet, a local wireless network, or a dedicated IoT protocol. It is used widely in environmental monitoring, health informatics, smart-city research, industrial systems, and behavioral science to capture objective, high-frequency data without requiring manual recording by participants or observers. | 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. |
| ScholarGateDatasett ↗ |
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