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Sensors, IoT and observation networks

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. Critical Infrastructure stewards this page for the Nexus Consortium.

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01Workspace

The workspace for Sensors, IoT and observation networks.

Telemetry network

The Global Centre for Risk and Innovation (GCRI) · Sensors, IoT and observation · Run R-7633 · Example interface, not a record

  • Channels reporting
    97of 100Normal
  • Median latency
    183ms
  • Records failing checks
    1.0per 1,000Watch
  • Backlog
    25min
Gaps on 5 channelsGCRI · GCRI

Latency, p95

Events

  1. 06:35WarnChannel C-86 stale
  2. 07:15InfoSchema v4 accepted
  3. 08:33ErrorChecksum mismatch, batch quarantined
  4. 08:59DoneReplay complete
Telemetry network.Illustrative interface; not live data.

GCRI · Telemetry network. Telemetry network: example names and illustrative values, not a record.

02The method

What sensors, IoT and observation networks covers.

  1. 01

    Core methods

    sensor network design; edge data processing; calibration and drift correction; anomaly detection; data fusion; telemetry management.

  2. 02

    Data and instruments

    environmental monitoring stations; structural health sensors; smart meters; low-cost air quality sensors; industrial control telemetry.

  3. 03

    Validation

    Sensors are calibrated against reference instruments and checked through co-location and redundancy. Data quality flags and automated range checks catch faults.

  4. 04

    Limits

    Sensor drift, power loss and connectivity gaps degrade data during the events of most interest. Dense networks raise security and privacy concerns.

  5. 05

    Use in risk decisions

    Observation networks enable early warning and condition-based maintenance. They reveal how stresses in one system appear in connected systems in near real time.

03Geography

Hazards and exposure worldwide.

Flood water, population density and active fires across the world, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards and exposure, for this page

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
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
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
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 and overpass timing leave gaps)
Source
VIIRS NOAA-20 375 m (FIRMS) (NASA LANCE FIRMS)
Provider
NASA LANCE FIRMS
Licence
NASA open data
Vintage
daily; Satellite detection; clouds and overpass timing leave gaps
Newest at build
2026-10-13
Resolution
about 500 m grid
Uncertainty
Satellite hot-spot detections for the day shown, including gas flares and industrial heat; not a confirmed fire, an incident report or a warning.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (persistent cloud still leaves gaps)
Source
VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
8-day; Satellite 8-day composite; persistent cloud still leaves gaps
Newest at build
2026-09-14
Resolution
about 1 km grid
Uncertainty
Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

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.

Hazards and exposure, framed on the world.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 · U.S. Geological Survey · 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 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
  • 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
  • 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
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles 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.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • 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 (11 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

  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026

04Reference practice

Sensor networks follow WMO observing standards and device security baselines.

ReferenceWhat it sets
WMO Integrated Global Observing System (WIGOS)Standards and metadata for observing stations
IEC 62443Security of connected industrial devices
OGC SensorThings APIInteroperable access to sensor data
05Process

How the process runs.

Process

Sensors, IoT and observation networks: Hazard, exposure, vulnerability

In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system’s exposure and vulnerability, and responses can add risk.

  1. 01 Drivers

    1. 01HazardDataset · Programme ownerThe potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.
    2. 02ExposureDataset · Programme ownerThe presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.
    3. 03SensitivityDataset · Programme ownerThe degree to which a system or species is affected, either adversely or beneficially, by climate variability or change.
    4. 04Capacity to cope and adaptDataset · Programme ownerThe ability of systems, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.

  2. 02 Assessment

    1. 05Risk from responsesComponent · Programme ownerRisks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.
    2. 06VulnerabilityComponent · Programme ownerThe propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.
    3. 07RiskModel · The Global Centre for Risk and InnovationRisk results from dynamic interactions between hazards and the system’s exposure and vulnerability. Each may be uncertain and change.

  3. 03 Response

    1. 08Risk management and responseStep · Programme ownerRisk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.
    2. 09Residual riskRecord · Programme ownerThe climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.

  4. 04 Decision

    1. 10Decision by the responsible partyDecision · Programme ownerThe framing names no single decision maker. Decisions on responses rest with the responsible party under its own rules.

Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.

06In systemic risk work

Dense data, uneven quality.

Low-cost sensors and connected devices can fill observation gaps in cities, rivers and infrastructure. Their value depends on calibration, maintenance and metadata, all of which decay without funding. Connected sensors in critical systems are also an attack surface.

  • Record calibration history and drift.
  • Flag gaps from device failure distinctly from true zero values.
  • Assess the security of sensors embedded in control systems.
  • Applications on Critical Infrastructure and Water Nexus.
07Applied in

Platforms that apply this method.

08Stewardship

Who maintains this page.

Critical Infrastructure stewards sensors, IoT and observation networks: it keeps this description current and reviews how the method is applied across the platforms.

09Sources

Where this page’s references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

10Questions

Questions and answers.

Why does one institution host every method page?
GCRI hosts the STEM Commons so that each method has one canonical description. Platforms link to it rather than repeat it, which keeps definitions consistent.
How do I suggest a correction to a method page?
Write to the integrity concern form with the passage and the reason.
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