Сравнение методов

	Сбор данных с сетевых датчиков	Сбор данных с помощью мобильных датчиков
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)	Mid-2000s (smartphone-era formalization ~2006–2010)
Автор метода	Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)	Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing
Тип	Quantitative / mixed-mode data collection technique	Passive and active quantitative data collection technique
Основополагающий источник	Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.	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.
Другие названия	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
Связанные	6	4
Сводка	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.