手法を比較

	オンラインセンサーデータ収集	縦断的センサーデータ収集
分野	調査方法論	調査方法論
系統	Process / pipeline	Process / pipeline
提唱年	Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)	1990s–2000s (accelerated with IoT and wearable devices from ~2010)
提唱者	Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)	Emerging from ambulatory assessment and wearable technology research communities
種類	Quantitative / mixed-mode data collection technique	Longitudinal quantitative/mixed data collection technique
原典	Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.	Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.]
別名	networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition	long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection
関連	6	3
概要	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.	Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design.