Detect Telemetry Anomalies on IoT Devices

v20260707

iot-anomalies

This tool detects and classifies anomalies (such as spikes, drift, flatlines, or pattern breaks) in the telemetry data streamed from Cognitum Seed IoT devices. It utilizes Z-score analysis to quantify the deviation from normal operational metrics. Use this when investigating unexpected device behavior, validating stability before firmware advancements, or triaging large-scale fleet health alerts.

IoT Telemetry Anomaly Detection Monitoring Diagnostics Z-score

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Overview

Run Z-score anomaly detection on a device's recent telemetry.

Steps:

npx -y -p @claude-flow/plugin-iot-cognitum@latest cognitum-iot anomalies DEVICE_ID
Review detected anomaly types (spike, flatline, drift, oscillation, pattern-break, cluster-outlier)
If score > 0.9, recommend quarantine
Store anomaly pattern for learning: mcp__claude-flow__memory_store({ key: "iot-anomaly-DEVICEID", value: "TYPE at SCORE", namespace: "iot-anomalies" })

Info

Category Data Science

Name iot-anomalies

Version v20260707

Size 813B

Source ruvnet/ruflo

Updated At 2026-07-09