Σύγκριση μεθόδων

	Ηλεκτρονική Συλλογή Δεδομένων Αισθητήρων	Συλλογή Δεδομένων Αισθητήρων
Πεδίο	Μεθοδολογία Επισκοπήσεων	Μεθοδολογία Επισκοπήσεων
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)	1990s–2000s (widespread deployment with IoT ~2000s)
Δημιουργός	Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Τύπος	Quantitative / mixed-mode data collection technique	Quantitative / mixed data collection technique
Θεμελιώδης πηγή	Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Εναλλακτικές ονομασίες	networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Συναφείς	6	5
Σύνοψη	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.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.