So sánh phương pháp

	Thu thập dữ liệu cảm biến trực tuyến	Thu thập dữ liệu dựa trên API
Lĩnh vực	Phương pháp luận khảo sát	Phương pháp luận khảo sát
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)	2000s–2010s (formalized as a research method)
Người khởi xướng	Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)	Emerged from computational social science and web 2.0 platform practices
Loại	Quantitative / mixed-mode data collection technique	Digital data collection technique
Công trình gốc	Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
Tên gọi khác	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
Liên quan	6	5
Tóm tắt	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.