Sensors and the industrial internet of things provide the data that automation, quality control and maintenance depend on. In this field they enable condition monitoring, predictive maintenance and traceability, withکوالیتیی داتاand security managed.
What sensors, IoT and observation networks does here.
Sensors, IoT and observation networks collect continuous measurements of physical, environmental and operational conditions. They provide the real-time inputs thatئاگادارکردنەوەی پێشوەختand asset monitoring depend upon. In industry and robotics, work and skills and trade, resources and supply chains, observation networks enable early warning and condition-based maintenance. They reveal how stresses in one system appear in connectedسیستەمەin near real time. The canonical description is onSTEM Commons.
Condition monitoring
Vibration, temperature and current sensors track the health of motors, pumps and bearings, flagging deterioration before failure.
In-line quality inspection
Sensors and machine vision inspect products during production, detecting defects earlier and more consistently than end-of-line checks.
Securing connected sensors
Industrial IoT devices are inventoried, segmented and updated so that they do not become entry points for attacks.
03جوگرافیا
ڕووبەڕووبوونی ژێرخان لە ئەڵمانیا.
ڕووناکی شەو، چڕی دانیشتووان و ئاوی لافاو بەسەر ئەڵمانیا، لە NASA GIBS. هەر چینێک بەرواری خۆی هەیە.
نەخشە کارلێککارەکە JavaScript پێویستە؛ لیستی چینەکان و سەرچاوەکان لە خوارەوەن.
We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified).داتای کراوەی NASA
We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified).داتای کراوەی NASA
ڕووناکیی شەوی VIIRS Black Marble، NASA Goddard Space Flight Center؛ بڵاودەکرێتەوە لەلایەن NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC)؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). داتای کراوەی NASA
Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). doi:10.7927/H49C6VHW. CC BY 4.0
بەرهەمەکانی زەویی MODIS (ئیندێکسی ڕووەک، بەرهەمی سەرەتایی گشتی، داپۆشراوی زەوی)، NASA LP DAAC و LANCE؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). داتای کراوەی NASA
ASTER GDEM بەرهەمی METI و NASA ە (ASTER Global Digital Elevation Model V003، NASA LP DAAC)؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). ناوهێنان بەم شێوەیە: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. داتای کراوەی NASA
ڕووداوە ڕاستەوخۆکان: NASA Earth Observatory Natural Event Tracker (EONET)؛ ڕووداوەکان نیشانەدەرن و بەستەری سەرچاوەی ڕەسەنی خۆیان دەهێڵنەوە.
ڕاستەوخۆ: NASA EONET ڕووداوەکان نیشانەدەرن، نەک ئاگادارکردنەوەی دەسەڵات.
Observed (EO): NASA/NOAA VIIRS (Suomi NPP). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
جۆری داپۆشەری زەوی (IGBP)، ساڵانە: داپۆشەری زەویی MODIS Terra و Aqua (MCD12Q1)، NASA LP DAAC · 2024
ئیندێکسی ڕووەکی باشترکراو، 8 ڕۆژی بزۆک: MODIS Terra EVI، NASA LANCE · 13ی تشرینی یەکەمی 2026
04داتا
What the method consumes.
Datasets and records used by sensors, IoT and observation networks on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
داتا
ڕۆڵ
ناونیشان
Sensor telemetry
Condition and quality
چاودێریکراو
Maintenance records
Ground truth
چاودێریکراو
Device inventories
Security
DECLARED
05شبیهسازی دووانە و زنجیرەیی
Model Industrial sensing and IoT as a system. Run the cascade.
Assembled from theNexus Standardsarea profileپیشەسازی و بەرهەمهێنان: the hazards it names, theسیستەمەand sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the fullSystem Builder.
NRS-X10 سەلامەتیی کیمیایی، پیشەسازی، ئەتۆمی و تیشکی
NRS-H54 خێزانی مەترسیدەر: تێکچوونی سیگناڵ و پلاتفۆرم (NHS-1 cyber.platform)
NRS-K14 پڕۆفایلی پاکێتی بوار: بەرهەمهێنان و پیشەسازی (DOMAIN-014)
چاودێری
eMARS Major Accident Reporting System (listed for reference)
World Development Indicators (listed for reference)
ILOSTAT (listed for reference)
Manufacturing value added as a proportion of GDP and per capita
CO2 emission per unit of value added
Manufacturing, value added (% of GDP)
Direct economic loss to all other damaged or destroyed productive assets attributed to disasters
جوگرافیای نموونە
The Strait of Malacca: 227 hexagonal cells
ڕووناکییەکانی شەو ئەمشەو، پچڕانی کارەبا (VIIRS Day/Night Band)، NASA GIBS
ئاوی ڕووی زەوی لە ڕێگەی هەورەوە، 30 م (ڕادارەی OPERA DSWx-S1)، NASA GIBS
ڕێژەی بارین، نیو کاتژمێری ڕابردوو (IMERG)، NASA GIBS
Surface soil moisture (SMAP), NASA GIBS
06پشتڕاستکردنەوە
How results are checked.
Sensors are calibrated against reference instruments and checked through co-location and redundancy.Data qualityflags and automated range checks catch faults.
بپشکنە
How it is done
Sensor calibration
Sensors are calibrated and drift tracked
Prediction validation
Maintenance predictions are compared with actual failures
Device security review
Connected devices are assessed before deployment
Sensor drift, power loss and connectivity gaps degrade data during the events of most interest. Dense networks raise security and privacy concerns.
07ڕێگا و بەڵگە
How Industrial sensing and IoT is modelled, measured and tested.
The couplings between the systems in Industrial sensing and IoT: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
پرەنسیپە ڕێنماییەکانی FAIR · GO FAIR و FORCE11W3C PROV · World Wide Web Consortiumڕێنمایی بۆ مامەڵەی یەکسان لەگەڵ نادڵنیاییەکان · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
Industrial sensing and IoT: Source to evidence record
10 steps·4 قۆناغ·2 دەروازە
پرۆسە
Industrial sensing and IoT: Source to evidence record
A source is registered and assessed, its record labelled and classified, and a Digital Evidence Passport carries provenance; a correction reaches every dependent record.
01 Intake
01Identify the sourceSource · The source stewardDiscovery is not ingestion. Source identification records the source class, steward, institutional role and jurisdiction.
03Register the sourceهەنگاوRegistration makes a discovered source a governed participant in the evidence lifecycle. It implies no endorsement or certification.
02 Assessment
04Jurisdiction and rights classificationدەروازەRights classification identifies licence, consent, confidentiality and reuse limits. The result determines access and routing.
05Evidence quality assessmentهەنگاوQuality assessment decides whether a record is fit for a defined use. Quality is multidimensional and never a single score.
06Evidence labelهەنگاوEvery finding carries one of nine evidence labels. Missing data is shown as missing, never as zero.
03 Record
07Evidence classificationتۆمارClassification gives records lifecycle states, such as source-verified, public-safe or restricted. States can coexist and set evidence use.
08Source release and identifierتۆمار · NXS-GRX(canonical data, provenance and risk)A source release records rights, temporal coverage and source lineage. AUNIis allocated through a catalogue and never typed by hand.
09Digital Evidence PassportتۆمارThe passport records provenance, quality, rights, jurisdiction, permitted use and history, so evidence keeps its context when it travels.
04 Use and correction
02Correction reaches dependent recordsStep · NXS-GRX (canonical data, provenance and risk)A source correction propagates to dependent cuts and products without rewriting old results.
10پێداچوونەوەی سەلامەتی گشتیدەروازەPublic-safe review decides whether a record can be shared publicly. It is a distinct stage and is not validation.
Data protocols: evidence lifecycle· Data protocols: core data objects and evidence records · The Global Risks Index (NXS-GRX)Designed programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The data-protocol pages describe a design, not a running system.
10Stewardship
Who maintains the method.
ژێرخانی هەستیارstewards sensors, IoT and observation networks for the Consortium and reviews how it is applied across platforms.
What are the limits of sensors, IoT and observation networks in this field?
Sensor drift, power loss and connectivity gaps degrade data during the events of most interest. Dense networks raise security and privacy concerns.
How are results checked?
Sensors are calibrated against reference instruments and checked through co-location and redundancy. Data quality flags and automated range checks catch faults.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Reportsis the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIپیشەسازیی پێشکەوتوو و ڕۆبۆتیکتەکنەلۆجیای ئێکسپۆنێنشیاڵمەترسی و خۆڕاگریی کەرتی جیهانی
Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.
This site uses only strictly necessary cookies to work and to remember your privacy choices. Nothing loads from other companies, and we never sell or share your personal information.
Details
This site uses only strictly necessary cookies to work and to remember your privacy choices. Nothing loads from other companies, and we never sell or share your personal information. We run no advertising. You can review your choices at any time through Privacy choices at the foot of every page.