fleet-management

name	fleet-management
description	Device provisioning, OTA update strategy, and fleet-scale monitoring Use when this capability is needed.
metadata	{"author":"dtsong"}

Fleet Management

Purpose

Design the fleet management infrastructure for an IoT device fleet, including device provisioning, OTA firmware update strategy, telemetry aggregation, and fleet-scale monitoring.

Inputs

• Expected fleet size (current and projected)
• Device hardware capabilities (storage, connectivity, compute)
• Update frequency requirements
• Monitoring and alerting requirements
• Regulatory requirements (safety-critical updates, rollback mandates)

Process

Step 1: Design Device Provisioning

Plan how devices go from factory to operational:

• Identity: Unique device ID (hardware serial, provisioned certificate)
• Authentication: Device certificates (X.509), pre-shared keys, or cloud-provisioned tokens
• Registration flow: First-boot sequence, cloud registration, owner assignment
• Zero-touch: Can a device self-provision without manual intervention?
• Factory integration: What happens on the manufacturing line?

Step 2: Design OTA Update System

Plan the firmware update lifecycle:

• Partition scheme: A/B (dual partition for atomic swap), single with rollback region
• Update delivery: Pull (device checks periodically) vs push (server initiates)
• Delta updates: Full firmware image vs binary diff (saves bandwidth)
• Integrity verification: Cryptographic signature verification before applying
• Rollback mechanism: Automatic rollback if new firmware fails health check
• Staged rollout: Canary (1%) → early (10%) → general (100%) with hold gates

Step 3: Design Telemetry Pipeline

Plan how device data reaches the cloud:

• Data types: Health metrics (battery, signal, temperature), application data, error reports
• Aggregation: On-device pre-aggregation to reduce bandwidth (send averages, not raw samples)
• Transport: MQTT topics, HTTP batch uploads, or store-and-forward
• Cloud ingestion: Message broker → stream processing → storage
• Retention: Hot (real-time queries), warm (weekly), cold (archive)

Step 4: Design Monitoring and Alerting

Plan fleet-scale observability:

• Health dashboards: Fleet-wide metrics (online %, firmware version distribution, battery levels)
• Anomaly detection: Devices reporting unusual values, sudden offline clusters
• Alert thresholds: Battery < 10%, signal < -90dBm, error rate > 1%, offline > 24h
• Group operations: Query and act on device groups (by firmware version, region, owner)

Step 5: Design Remote Management

Plan remote device operations:

• Configuration updates: Push configuration changes without firmware update
• Remote diagnostics: Request debug logs, trigger self-test, read sensor state
• Remote actions: Reboot, factory reset, enter recovery mode
• Access control: Who can perform which operations on which devices

Step 6: Plan Scaling Strategy

Design for fleet growth:

• Connection management: Connection limits per broker, load balancing
• Update infrastructure: CDN for firmware binaries, rate limiting downloads
• Database design: Time-series storage for telemetry, device registry scaling
• Cost modeling: Per-device cloud cost at 1K, 10K, 100K, 1M devices

Output Format

# Fleet Management Architecture

## Provisioning Flow

[Factory] → [Flash firmware + certificate] → [First boot] → [Cloud registration] → [Owner assignment] → [Operational]

| Step | Method | Duration | Manual? |
|------|--------|----------|---------|
| Identity | X.509 certificate | Factory | No |
| Registration | MQTT first-connect | <30s | No |
| Owner assignment | QR code scan | User-initiated | Yes |

## OTA Update Strategy
| Aspect | Approach |
|--------|----------|
| Partition scheme | A/B dual-partition |
| Delivery | Pull, 6-hour check interval |
| Format | Delta updates (bsdiff) |
| Verification | Ed25519 signature |
| Rollback | Automatic on 3 failed health checks |
| Staged rollout | 1% → 10% → 50% → 100% with 24h holds |

## Telemetry Pipeline

[Device] → [MQTT] → [Message Broker] → [Stream Processor] → [Time-Series DB] → [Dashboard]

| Data Type | Frequency | Aggregation | Retention |
|-----------|-----------|-------------|-----------|
| Health | 5 min | On-device avg | 90 days |
| Errors | Event-driven | None | 1 year |
| Application | 30 sec | 1-min rollups | 30 days |

## Monitoring Dashboard
| Metric | Threshold | Alert |
|--------|-----------|-------|
| Fleet online % | < 95% | Warning |
| Firmware current % | < 80% | Info |
| Battery critical | < 5% | Critical |
| Error rate | > 1% | Warning |

## Scaling Projections
| Fleet Size | Monthly Cost | Key Bottleneck |
|-----------|-------------|----------------|
| 1,000 | $X | None |
| 10,000 | $X | MQTT connections |
| 100,000 | $X | Telemetry storage |

Quality Checks

• Provisioning flow is zero-touch (no manual steps per device)
• OTA updates are cryptographically signed and verified
• Rollback is automatic — a bad update doesn't brick the fleet
• Staged rollout has hold gates between stages
• Telemetry pipeline handles device-side aggregation to reduce bandwidth
• Monitoring has defined thresholds and alert escalation paths
• Cost model scales linearly (not exponentially) with fleet size

Evolution Notes

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