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Live in ProductionConstruction / Industrial Assets
📍

LoRa Asset Tracking System — Indoor & Outdoor Equipment Monitoring

A LoRa-based asset tracking system for a construction conglomerate monitoring 1,200 pieces of equipment across 8 sites — reducing equipment search time from 2 hours to 2 minutes and eliminating $400K/year in replacement purchases for "lost" assets.

< 2 min
Search Time
$400K/yr
Savings
1,200+
Assets

⚠The Problem

A construction company managing 1,200 pieces of equipment (generators, compressors, scaffolding, tools) across 8 active sites was spending 2+ hours per day per site searching for misplaced equipment. Equipment was frequently moved between sites without authorization. "Missing" assets were repurchased, costing $400K annually — most were later found on wrong sites. Equipment maintenance was on a calendar schedule regardless of actual usage hours.

💡Our Solution

We attached compact LoRa asset tags (IP67 waterproof, 3-year battery) to every piece of equipment using magnetic mounts. On each site, 4–8 LoRa anchor gateways provide coverage. A Python RSSI triangulation engine calculates equipment position with 5–10m accuracy (zone-level for outdoor, room-level for indoor). The accelerometer detects unauthorized movement and fires immediate tamper alerts. Usage hours are calculated from movement patterns, feeding a usage-based maintenance schedule. The React dashboard shows all 1,200 assets on a zoned site map — searchable by name, type, or status.

🔗System Architecture

Asset Tags (LoRa) → Anchor Gateways → MQTT → Node.js Backend → PostgreSQL + Redis → React Site Map

Tech Stack

Hardware
  • ESP32 + LoRa asset tags (compact, IP67)
  • LiFePO4 battery (3-year life)
  • Accelerometer for tamper/movement detection
  • LoRa anchor gateways (indoor + outdoor)
  • Magnetic mount for metal assets
Communication
  • LoRa 433/868MHz (no cellular required)
  • RSSI-based position estimation
  • MQTT backhaul via site Wi-Fi or 4G
Cloud
  • Node.js backend (asset registry)
  • PostgreSQL (location history)
  • Redis (real-time asset positions)
  • Python RSSI triangulation engine
Frontend
  • React site map dashboard
  • Asset search and filter
  • Utilization rate analytics
  • Maintenance schedule integration

Key Features

Real-time location of 1,200+ assets across 8 sites
Asset search: find any equipment on a map in < 2 minutes
Tamper / unauthorized movement alerts via SMS + push
Cross-site transfer authorization workflow
Usage-hour-based maintenance scheduling
Utilization rate analytics per asset class
3-year battery life on asset tags — no charging logistics
Geofenced site boundary alerts for theft prevention

Results Delivered

  • Asset search time reduced from 2 hours to 2 minutes
  • $400K/year equipment replacement purchases eliminated
  • Cross-site unauthorized transfers caught: 34 incidents in first 6 months
  • Maintenance compliance rate improved from 60% to 95%
  • 1,200 assets tracked across 8 sites on a single platform

Technologies

LoRaESP32RSSI TriangulationReactNode.jsGIS Map

Who This Is For

Construction companies, warehouse operators, hospitals (equipment tracking), oil & gas sites

Need a similar solution?

We've built production IoT systems like this across 15+ countries. Let's talk about yours.

Frequently Asked Questions

What technology does this lora asset tracking system use?+

LoRa Asset Tracking System — Indoor & Outdoor Equipment Monitoring is built on LoRa, ESP32, RSSI Triangulation, React, Node.js. The stack was chosen for real-world reliability in a production deployment — full details are in the tech stack section.

Can CodeCaracal build a similar system for us?+

Yes. The same team that designed and deployed this construction / industrial assets system takes on new projects across the stack — firmware, connectivity, cloud and dashboards. Tell us your requirements and we will architect a solution for your environment.

Is this case study a real deployed system?+

Yes — every case study on this site describes a production system we designed and shipped. The problem, architecture, features and metrics are from the actual deployment.

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