Phillips Hue Bridge and Google Cloud Monitoring Integration

Input and output integration overview

This plugin gathers status from Hue Bridge devices using the CLIP API interface.

The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.

Integration details

Phillips Hue Bridge

The Hue Bridge plugin allows users to gather real-time status from Philips Hue Bridge devices utilizing the CLIP API interface. By communicating with Hue Bridges, this plugin is capable of retrieving various metrics related to home lighting and environmental conditions. It offers multiple schemes for accessing the bridges, such as local LAN, cloud, and mDNS, ensuring flexibility in deployment scenarios. The plugin can handle diverse configurations such as room assignments for devices, which optimizes the evaluation of statuses, especially in environments with many devices. Furthermore, it provides various monitoring metrics applicable to lights, temperature sensors, motion sensors, and device power status, thereby enabling comprehensive insights into a smart home setup. The configuration options allow users to tailor their connections to optimize performance and security, including optional TLS configurations for secure communication.

Google Cloud Monitoring

This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a namespace variable and metric key, facilitating organized data management. However, users are encouraged to use the official naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.

Configuration

Phillips Hue Bridge

[[inputs.huebridge]]
  ## URL of bridges to query in the form ://:@
/
  ## See documentation for available schemes.
  bridges = [ "address://:@/" ]

  ## Manual device to room assignments to apply during status evaluation.
  ## E.g. for motion sensors which are reported without a room assignment.
  # room_assignments = { "Motion sensor 1" = "Living room", "Motion sensor 2" = "Corridor" }

  ## Timeout for gathering information
  # timeout = "10s"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  # tls_key_pwd = "secret"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false
</code></pre>

Google Cloud Monitoring

[[outputs.stackdriver]]
  ## GCP Project
  project = "project-id"

  ## Quota Project
  ## Specifies the Google Cloud project that should be billed for metric ingestion.
  ## If omitted, the quota is charged to the service account’s default project.
  ## This is useful when sending metrics to multiple projects using a single service account.
  ## The caller must have the `serviceusage.services.use` permission on the specified project.
  # quota_project = ""

  ## The namespace for the metric descriptor
  ## This is optional and users are encouraged to set the namespace as a
  ## resource label instead. If omitted it is not included in the metric name.
  namespace = "telegraf"

  ## Metric Type Prefix
  ## The DNS name used with the metric type as a prefix.
  # metric_type_prefix = "custom.googleapis.com"

  ## Metric Name Format
  ## Specifies the layout of the metric name, choose from:
  ##  * path: 'metric_type_prefix_namespace_name_key'
  ##  * official: 'metric_type_prefix/namespace_name_key/kind'
  # metric_name_format = "path"

  ## Metric Data Type
  ## By default, telegraf will use whatever type the metric comes in as.
  ## However, for some use cases, forcing int64, may be preferred for values:
  ##   * source: use whatever was passed in
  ##   * double: preferred datatype to allow queries by PromQL.
  # metric_data_type = "source"

  ## Tags as resource labels
  ## Tags defined in this option, when they exist, are added as a resource
  ## label and not included as a metric label. The values from tags override
  ## the values defined under the resource_labels config options.
  # tags_as_resource_label = []

  ## Custom resource type
  # resource_type = "generic_node"

  ## Override metric type by metric name
  ## Metric names matching the values here, globbing supported, will have the
  ## metric type set to the corresponding type.
  # metric_counter = []
  # metric_gauge = []
  # metric_histogram = []

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Additional resource labels
  # [outputs.stackdriver.resource_labels]
  #   node_id = "$HOSTNAME"
  #   namespace = "myapp"
  #   location = "eu-north0"

Input and output integration examples

Phillips Hue Bridge

1. Automated Lighting Control Based on Room Occupancy: Utilize the Hue Bridge plugin to monitor motion sensors within various rooms of a home. When motion is detected, the system can automatically trigger the lights to turn on, providing convenience and energy efficiency. This integration could significantly enhance user experience and preferences, adapting the lighting to occupancy levels without manual intervention.

2. Environmental Monitoring in Smart Homes: Implement the Hue Bridge plugin to track temperature and light levels within the house. By continuously monitoring these metrics, users can create a comfortable indoor climate, adjusting heating and cooling systems based on temperature trends or activating lights based on light levels detected. This data-driven approach leads to smart home automation that responds to actual environmental conditions.

3. Integration with Home Automation Systems: Leverage this plugin to integrate Philips Hue Bridge statistics into broader home automation frameworks. For example, collecting light and temperature data can feed into a centralized dashboard that provides homeowners with insights about their energy usage patterns. Environments can be programmed to respond proactively to user habits, promoting efficiency and energy conservation.

4. Battery Monitoring for Smart Devices: Use the Hue Bridge plugin to monitor battery levels across various connected smart devices. By being alerted about low battery states, homeowners can take timely actions to replace or recharge devices, preventing outages and ensuring smooth operation of their smart home systems.

Google Cloud Monitoring

1. Multi-Project Metric Aggregation: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.

2. Anomaly Detection Setup: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.

3. Dynamic Resource Labeling: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.

4. Custom Metric Visualization Dashboards: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.

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