Linganisha mbinu

	Ukusanyaji wa Data wa Kihisi mtandaoni	Ukusanyaji wa Data za Kihisi
Nyanja	Metodolojia ya Dodoso	Metodolojia ya Dodoso
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)	1990s–2000s (widespread deployment with IoT ~2000s)
Mwanzilishi	Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Aina	Quantitative / mixed-mode data collection technique	Quantitative / mixed data collection technique
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	5
Muhtasari	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.