Condition monitoring
Vibration, temperature and current sensors track the health of motors, pumps and bearings, flagging deterioration before failure.
GCRI · Advanced Industry and Robotics
Industrial sensing and IoT
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, with data quality and security managed.
01Register
Industrial sensing and IoT on the record.
Method recordSTEM method
- MethodSensors, IoT and observation networks
- StewardCritical Infrastructure
- Applied inAdvanced Industry and Robotics
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
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 that early warning and 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 connected systems in near real time. The canonical description is on the STEM Commons.
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.
03Geography
Infrastructure exposure in Germany.
Night lights, population density and flood water over Germany, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Infrastructure exposure, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Infrastructure exposure, framed on Germany.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA/NOAA VIIRS · NASA JPL OPERA DIST-ALERTSources and licences
- 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). NASA open data
- 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). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- 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. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: 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. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- 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).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (12 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
04Data
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 in the source register.
| Data | Role | Label |
|---|---|---|
| Sensor telemetry | Condition and quality | OBSERVED |
| Maintenance records | Ground truth | OBSERVED |
| Device inventories | Security | DECLARED |
05Twin and cascade simulator
Model Industrial sensing and IoT as a system. Run the cascade.
Assembled from the Nexus Standards area profile Industry and manufacturing: the hazards it names, the systems and 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 full System Builder.
Hazards
- Natech event
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H50Hazard family: Industrial and process accidents (NHS-1 infra.industrial)NRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-X10Chemical, industrial, nuclear and radiological safetyNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-K14Domain pack profile: Manufacturing industrial (DOMAIN-014)
Observation
- 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
Example geography
- The Strait of Malacca: 227 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
06Validation
How results are checked.
Sensors are calibrated against reference instruments and checked through co-location and redundancy. Data quality flags and automated range checks catch faults.
| Check | 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.
07Methods and evidence
How Industrial sensing and IoT is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
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.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
08Connections
Topics that rely on this method.
09Process
How the process runs.
Process
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 sourceStepRegistration makes a discovered source a governed participant in the evidence lifecycle. It implies no endorsement or certification.
02 Assessment
- 04Jurisdiction and rights classificationGateRights classification identifies licence, consent, confidentiality and reuse limits. The result determines access and routing.
- 05Evidence quality assessmentStepQuality assessment decides whether a record is fit for a defined use. Quality is multidimensional and never a single score.
- 06Evidence labelStepEvery finding carries one of nine evidence labels. Missing data is shown as missing, never as zero.
03 Record
- 07Evidence classificationRecordClassification gives records lifecycle states, such as source-verified, public-safe or restricted. States can coexist and set evidence use.
- 08Source release and identifierRecord · NXS-GRX (canonical data, provenance and risk)A source release records rights, temporal coverage and source lineage. A UNI is allocated through a catalogue and never typed by hand.
- 09Digital Evidence PassportRecordThe 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.
- 10Public-safe reviewGatePublic-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.
Critical Infrastructure stewards sensors, IoT and observation networks for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
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.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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