Telemetry data

Telemetry data

Tesenso IoT Cloud provides a rich set of features related to time-series data:

  • Collect data from devices using various protocols and integrations;

  • Store time series data in SQL (PostgreSQL) or NoSQL (Cassandra or Timescale) databases;

  • Query the latest time series data values or all data within the specified time range with flexible aggregation;

  • Subscribe to data updates using WebSockets for visualization or real-time analytics;

  • Visualize time series data using configurable and highly customizable widgets and dashboards;

  • Filter and analyze data using flexible Rule Engine;

  • Generate alarms based on collected data;

  • Forward data to external systems using External Rule Nodes (e.g. Kafka or RabbitMQ Rule Nodes).

This guide provides an overview of the features listed above, and some useful links to get more details.

Data points

Tesenso IoT Cloud internally treats time-series data as timestamped key-value pairs. We call single timestamped key-value pair a data point. Flexibility and simplicity of the key-value format allow easy and seamless integration with almost any IoT device on the market. Key is always a string and is basically a data point key name, while the value can be either string, boolean, double, integer or JSON.

Examples below use internal data format. The device itself may upload data using various protocols and data formats. See Time-series data upload API for more details.

The following JSON contains 5 data points: temperature (double), humidity (integer), hvacEnabled (boolean), hvacState (string) and configuration (JSON):

 "temperature": 42.2, 
 "humidity": 70,
 "hvacEnabled": true,
 "hvacState": "IDLE",
 "configuration": {
    "someNumber": 42,
    "someArray": [1,2,3],
    "someNestedObject": {"key": "value"}

You may notice that the JSON listed above does not have a timestamp information. In such case, Tesenso IoT Cloud uses current server timestamp. However, you may include timestamp information into the message. See example below:

  "ts": 1527863043000,
  "values": {
    "temperature": 42.2,
    "humidity": 70

Time-series data upload API

You may use built-in transport protocol implementations:

  • MQTT API reference

  • CoAP API reference

  • HTTP API reference

  • LwM2M API reference

Most of the protocols above support JSON, Protobuf or own data format. For other protocols, please review “How to connect your device?” guide.

Data visualization

We assume you have already pushed time-series data to Tesenso IoT Cloud. Now you may use it in your dashboards. We recommend dashboards overview to get started. Once you are familiar how to create dashboards and configure data sources, you may use widgets to visualize either latest values or real-time changes and historical values. Good examples of widgets that visualize latest values are digital and analog gauges, or cards. Charts are used to visualize historical and real-time values and maps to visualize movement of devices and assets.

You may also use input widgets to allow dashboard users to input new time-series values using the dashboards.

Rule engine

The Rule Engine is responsible for processing all sorts of incoming data and event. You may find most popular scenarios of using attributes within rule engine below:

Generate alarms based on the logical expressions against time-series values

Use alarm rules to configure most common alarm conditions via UI or use filter nodes to configure more specific use cases via custom JS functions.

Modify incoming time-series data before they are stored in the database

Use message type switch rule node to filter messages that contain “Post telemetry” request. Then, use transformation rule nodes to modify a particular message.

Calculate delta between previous and current time-series value

Use calculate delta rule node to calculate power, water and other consumption based on smart-meter readings.

Fetch previous time-series values to analyze incoming telemetry from device

Use originator telemetry rule node to enrich incoming time-series data message with previous time-series data of the device.

Fetch attribute values to analyze incoming telemetry from device

Use enrichment rule nodes to enrich incoming telemetry message with attributes of the device, related asset, customer or tenant. This is extremely powerful technique that allows to modify processing logic and parameters based on settings stored in the attributes.

Use analytics rule nodes to aggregate data for related assets

Use analytics rule nodes to aggregate data from multiple devices or assets.

Useful to calculate total water consumption for the building/district based on data from multiple water meters.

Last updated