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| Pengumpulan Data Sensor Daring× | Pengumpulan Data Berbasis API× | |
|---|---|---|
| Bidang | Metodologi Survei | Metodologi Survei |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000) | 2000s–2010s (formalized as a research method) |
| Pencetus≠ | Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000) | Emerged from computational social science and web 2.0 platform practices |
| Tipe≠ | Quantitative / mixed-mode data collection technique | Digital data collection technique |
| Sumber perintis≠ | Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Alias | networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Terkait≠ | 6 | 5 |
| Ringkasan≠ | 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. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
| ScholarGateSet data ↗ |
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