{"product_id":"kit-pi-5-explainable-iot-anomaly-detection","title":"Pi 5 Explainable IoT Anomaly Detection","description":"\u003ch1\u003eBuild Interpretable AI for Safety-Critical IoT with the Raspberry Pi 5 SHAP Anomaly Detection Kit\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 8-10 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Explainable AI model deployment\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eWhen an IoT anomaly detector flags a temperature spike in a pharmaceutical cold chain or a pressure fluctuation in an industrial boiler, you need to know exactly which sensor features drove that alert — not a black-box guess. This kit puts SHAP (SHapley Additive exPlanations) on a Raspberry Pi 5, letting you train a neural network on multi-sensor data from three ESP32 nodes, then generate per-alert explanations that engineers and auditors can trust. It turns the Pi 5 into an edge explainability engine for safety-critical IoT, mirroring the interpretable AI workflows demanded in industrial compliance and research.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou will create a three-node IoT sensor mesh using ESP32 boards streaming temperature, humidity, gas, and vibration data to a Pi 5. On that Pi, you’ll train a lightweight autoencoder-based anomaly detector, then integrate SHAP to explain every alert in terms of individual sensor contributions. The result is a dashboard-like CLI report showing, for each anomaly, a ranked list of the top sensor features that pushed the model to trigger an alert — exactly what is needed for root-cause analysis in domains like cold storage auditing, machinery health monitoring, or environmental hazard detection.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy TensorFlow Lite models on Raspberry Pi 5 with NVMe acceleration for real-time inference\u003c\/li\u003e\n  \u003cli\u003eImplement SHAP explainability on edge devices to extract per-feature contributions from anomaly scores\u003c\/li\u003e\n  \u003cli\u003eBuild a multi-sensor IoT pipeline using ESP32, MQTT, and time-series preprocessing\u003c\/li\u003e\n  \u003cli\u003eApply interpretable AI principles to safety-critical systems, preparing for research or industry roles where model transparency is mandatory\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eESP32 Dev Board\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eVarious Sensors\u003c\/td\u003e\n\u003ctd\u003e6\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroUSB Cable\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e25\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eThis kit fits B.Tech ECE\/EEE students tackling final-year projects on explainable AI or industrial IoT, Smart India Hackathon participants building safety-critical solutions, and researchers at IITs, NITs, or VIT who need a reproducible edge AI testbed. It is also ideal for ATL Tinkering Lab mentors wanting an advanced, real-world AI interpretability demonstration for senior students. If you’ve completed basic Python and microcontroller courses and want to advance into interpretable machine learning on the edge, this is your project.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eThe AI companion provides real-time code troubleshooting and wiring checks; if it can’t resolve the issue, our engineers step in via WhatsApp within 24 hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior machine learning experience?\u003c\/summary\u003e\u003cp\u003eYou should understand Python and basic neural network concepts. The AI companion guides you through TensorFlow Lite conversion and SHAP integration, but this is an advanced project — some prior exposure to ML helps you move faster.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat sensor anomalies does this kit simulate?\u003c\/summary\u003e\u003cp\u003eThe included six sensors (temperature, humidity, MQ gas, vibration, light, and PIR) allow you to create realistic anomaly scenarios like cold storage breaches, machine vibration spikes, or unauthorized access — all with per-feature SHAP explanations.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I deploy this explainability pipeline in a real industrial setting?\u003c\/summary\u003e\u003cp\u003eYes, the architecture is designed to be portable. You’ll use MQTT with TLS, model serialisation, and a configurable SHAP explainer loop that can be adapted to rigid industrial gateways running Linux — a skill directly transferable to internships at Bosch, Siemens, or Indian defence labs.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eSHAP values on Pi 5 explain which sensor features triggered each anomaly alert — interpretable AI for safety-critical IoT.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/esp32-30-pin-development-board-cp2102-wifi-bluetooth\"\u003eESP32 Dev Board\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003eVarious Sensors x6\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/microusb-cable-1m-charging-data-cord-for-arduino-android\"\u003eMicroUSB Cable\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x25\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Explainable IoT Anomaly Detection?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Explainable IoT Anomaly Detection includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, ESP32 Dev Board, Various Sensors and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Explainable IoT Anomaly Detection?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. SHAP values on Pi 5 explain which sensor features triggered each anomaly alert — interpretable AI for safety-critical IoT. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Explainable IoT Anomaly Detection online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Explainable IoT Anomaly Detection is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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