Monitoring & Telemetry#

Embedded devices generate telemetry — heartbeats, sensor readings, log entries, error codes — but the value of that data depends entirely on the backend infrastructure that receives, stores, queries, and acts on it. A fleet of 10,000 devices publishing metrics every 60 seconds produces 14.4 million data points per day. Without purpose-built ingestion pipelines, time-series databases, and alerting systems, that stream of telemetry disappears into a void or overwhelms general-purpose infrastructure never designed for the write-heavy, append-only, time-correlated access patterns that IoT workloads demand.

This section covers the backend side of the monitoring equation: the infrastructure that turns raw device telemetry into searchable logs, queryable metrics, actionable alerts, and security event detection. The device side — what metrics to collect, heartbeat design, log formatting, per-device alerting thresholds — is covered in Fleet Monitoring.

The monitoring stack itself follows from the platform architecture decision. Cloud-managed deployments typically use cloud-native monitoring (CloudWatch, Azure Monitor, Amazon Timestream) with minimal operational overhead. Self-hosted deployments assemble Prometheus, Grafana, Alertmanager, and a time-series database (InfluxDB, TimescaleDB) into a monitoring stack that must be deployed and maintained alongside the broker and device management infrastructure.

The boundary between the two sides falls at the message broker. Fleet Monitoring describes what a device publishes to MQTT topics and how it manages its own telemetry budget. This section picks up from the broker onward: how subscribers route messages into log aggregators, how time-series databases store and downsample metric streams, how alerting pipelines decide when to page an engineer, and how security event monitoring detects anomalous behavior across the fleet.

What This Section Covers#

• Centralized Logging with ELK — Aggregating device logs with Elasticsearch, Logstash, and Kibana: MQTT-to-Logstash ingestion, index lifecycle management, KQL search patterns, and Grafana Loki as a lightweight alternative.
• Metrics & Dashboards — Prometheus and Grafana for IoT fleet metrics: bridging MQTT to the pull model, PromQL fleet queries, dashboard hierarchy design, and scaling with Thanos or Cortex.
• Alerting — Alertmanager configuration, route trees, fleet-wide vs. per-device rules, PagerDuty and OpsGenie integration, runbook linking, and alert fatigue management.
• Time-Series Databases — InfluxDB, TimescaleDB, and Amazon Timestream: data models, retention policies, downsampling strategies, and schema design for IoT workloads.
• Distributed Tracing — OpenTelemetry concepts, trace context propagation through MQTT and cloud functions, Jaeger architecture, and sampling strategies for IoT pipelines.
• SIEM & Security Monitoring — Security information and event management for IoT fleets: anomalous traffic detection, intrusion detection patterns, compliance logging, and incident response workflows.