The DOI matches its Crossref record.
Rotating Sensor for Multi-Direction Light Intensity Measurement
Abstract
Ambient light usually is heterogeneous due to the varying of daylight and occupancy illumination. The performance of many applications, such as intelligent lighting control, heavily depends on the light sensor system. It is necessary to have a sensor system that can measure the light intensity in different directions. A light sensors network, usually used in lighting con- trol system, requires many sensors located at multiple locations. It is difficult to change the number of sensor and the locations in the system. We introduce a novel system which uses only a single sensor rotated by a stepper motor to measure light intensity from different directions. After identified the sensor direction, the system measures the light intensity and transmits the data to a monitoring unit. The rotating sensor could be able to communicate with the Internet of Things data monitoring platform. We have performed experiments in a normal occupancy workroom. The results show that our sensor may measure light intensity in multiple directions. Besides, the sensor can detect whether some directions were blocked while firmly measuring light level on the other directions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The performance of smart lighting control depends on the light sensor.
“Hoang et al. [97] add that the performance of smart lighting control depends on the light sensor.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Hoang et al. [97]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 10
- 10.1109/JSEN.2010.2042442 · DOI
- 10.1145/1878431.1878448 · DOI
- 2019, Ubidots, Ubidots internet of things and cloud tools
- 10.1109/JSEN.2012.2228850 · DOI
- 10.1109/JSEN.2008.2004294 · DOI
- m, 2019, electronics, Bh1750 rohm semiconductor datasheet
- 10.1016/j.enbuild.2014.02.016 · DOI
- oreilly, 2005, Use of wireless sensor networks for fluorescent lighting control with daylight substitution, Proceedings of the workshop on Real-world wireless sensor networks
- e, 2019, Systems, Overview of ESP8266
- 10.1145/1098918.1098942 · DOI
Cited by
Sorted by citations
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“[97] add that the performance of smart lighting control depends on the light sensor.”
Background
Metadata
- Authors
- Hoang, D. A., Tung, T. T., Nguyen, C. M., & Nguyen, K. P.
- Affiliations
- Hanoi Open University (VN)
- Venue
- 2019 International Conference on System Science and Engineering (ICSSE)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 462-467
- OpenAlex
- W2971440095
- Cited by
- 6
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 1
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on System Science and Engineering (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Instrumentation › Sensor Technology and Measurement Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- ambient light sensing, intelligent lighting control, stepper motor, Internet of Things
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review