How InfluxDB Works with IoT Data

Why use InfluxDB for IoT telemetry?

  • Proven. Every IoT device generates time series data, and the explosion of these devices and the data they generate, fueled by the desire for more analytics, has driven the requirements for this type of data to be handled by a specialized platform. InfluxDB has proven its chops at some of the most demanding IoT customers in the world, such as Siemens and PTC, as a time series database at the core of their IoT stack.
  • Scalable. IoT data volumes and the appetite to consume them are rapidly growing. InfluxDB scales to ingest and index massive volumes of IoT telemetry, while providing real-time analytics and fast query response times.
  • Extensible. IoT telemetry comes in a wide variety of formats across many domains — legacy standards, new datatypes, and everything in between. InfluxDB can ingest a broad range of data formats. If you need a format that isn’t supported, InfluxDB’s extensibility lets you ingest it.
  • Flexible. InfluxDB’s Flux language is incredibly powerful for building IoT solutions, since it gives you a single language to both shape and query your IoT data, and does so at the database layer so that you don’t have to resort to expensive queries to download data to modify it.
  • Contextual. InfluxDB blends rich metadata, such as IoT asset information, into time series measurements, making it easier to derive insights on past, current and future performance of assets and processes.

Every IoT use case is a time series use case

  • Connected fleets: Solar power companies such as BBOXX regularly send measurements on the voltage produced by each of their installations, so they can tell when a particular one is in need of repair — sometimes before end customers themselves know.
  • Smart spaces : Office tower managers like Aquicore track the amount of energy consumed across all floors of a building, a “Smart City” example of optimizing the amount of energy required to provide a comfortable environment for occupants.
  • Smart products: Office water dispensers, for instance Bevi, measure the amount of flavoring used, so that their service personnel can automatically restock flavorings just before they run out.

IoT data architecture

  1. Hub only
  2. Edge and hub

How to plan your IoT data architecture

  • Past: How did we perform previously? What impact did this have on quality, efficiency, or quantity, and what changes (if any) does it suggest? This analysis can be especially useful when rolled up across multiple sites (factories, wind turbines, etc.).
  • Current: How are we performing right now? Are there corrective actions that need to be taken? These kinds of decisions are often taken by onsite operational staff.
  • Future: How are we likely to perform in the future, based on the data we’ve seen so far? This can help drive decisions on predictive maintenance, by using data on mileage or usage to determine when a piece of equipment will need to be repaired or replaced.

What’s new with InfluxDB and IoT

  • Telegraf, to acquire and enrich IoT telemetry data
  • InfluxDB Cloud, to persist, enrich, and analyze IoT data at the hub
  • InfluxDB OSS, to persist, enrich, and analyze IoT data at the edge

Acquire IoT telemetry data

  • Acquire data from the majority of commonly used IIoT (industrial IoT) telemetry protocols like OPC-UA;
  • Support open, standards-based protocols like MQTT; and
  • Provide extensibility to gather data from other protocols.
  • Telegraf OPC-UA plugin (connects to many systems, including PTC Kepware)
  • Telegraf Modbus plugin
  • Telegraf KNX plugin (coming soon in Telegraf 1.19)

Enrich IoT telemetry data

  • Metadata enrichment
  • Geospatial enrichment
  • Calculated values enrichment

Operate using local IoT telemetry data

Analyze IoT telemetry data

Our IoT product philosophy

Conclusion

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Al Sargent

Al Sargent

Occasional thoughts on tech, sailing, and San Francisco

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