Smart Energy Monitoring System for Industrial Power Distribution
A multi-unit industrial complex needed a centralized energy monitoring solution to track real-time power usage across its electrical distribution
Read more →- Industrial IoT
- LoRaWAN
- Manufacturing
Industrial IoT Solution for Smart Manufacturing
Real-time visibility and analytics across machines, environment, and energy—powered by LoRaWAN and Wi-Fi.
Case study ·
- 22%less machine downtime in 60 days
- 40%lower cabling cost
- 30+sensor nodes on 2 shop floors
Project overview
The brief
A multi-line manufacturing facility sought to digitize its operations by capturing real-time data from machines, electrical panels, and the shop-floor environment. They wanted to monitor machine health, ambient conditions, and energy usage to improve efficiency and reduce downtime.
Problem statement
What needed solving
- No centralized visibility into machine runtime, fault states, or power spikes.
- Manual tracking of temperature, humidity, and vibration data was inconsistent.
- Maintenance was reactive, leading to unplanned downtime.
- Existing wired solutions were costly and inflexible for plant layout.
- Needed scalable, wireless IoT with real-time alerts and dashboards.
Our solution
LoRaWAN + Wi-Fi Enabled IoT System
Kelectron designed a hybrid Industrial IoT (IIoT) system using LoRaWAN for long-range data capture and Wi-Fi for high-throughput nodes like energy meters.
| Module | Technology |
| Sensors | Vibration, temperature, humidity, current, voltage, RPM sensors |
| Connectivity | LoRaWAN (for remote, low-power devices), Wi-Fi (for real-time energy data) |
| Gateways | Multichannel LoRa gateway + Raspberry Pi edge node |
| Backend | MQTT Broker + Python FastAPI + MongoDB |
| Dashboard | Custom web dashboard + charting + alert engine |
Implementation Highlights
Deployed 30+ nodes across 2 shop floors
Designed custom low-power sensor nodes with STM32 and ESP32
Data transmitted every 10s–60s depending on criticality
Set up LoRaWAN network server and OTA configuration
Created rule-based alert system (e.g., high temp, abnormal vibration)
Enabled PDF reports and Excel exports for compliance teams
Results & Benefits
What changed for the client
- Reduced machine downtime by 22% in first 60 days
- Energy optimization strategies identified via load trend analytics
- Enabled predictive maintenance by correlating vibration + temperature spikes
- Fully wireless deployment reduced cabling cost by 40%
- Operators and managers now access data remotely via mobile dashboard
Results as reported on this client project. Outcomes vary with each plant, process and data set.
Technologies used
The stack behind the project
- MCUs
- STM32, ESP32
- Protocols
- LoRaWAN, MQTT, HTTP
- Connectivity
- Wi-Fi, RS485, BLE (optional)
- Software
- Python, FastAPI, MongoDB, Chart.js
- Cloud/Local
- Hybrid – edge analytics + optional cloud sync
About this project
What problem did the Industrial IoT Solution for Smart Manufacturing project solve?
A multi-line manufacturing facility sought to digitize its operations by capturing real-time data from machines, electrical panels, and the shop-floor environment. They wanted to monitor machine health, ambient conditions, and energy usage to improve efficiency and reduce downtime. No centralized visibility into machine runtime, fault states, or power spikes. Manual tracking of temperature, humidity, and vibration data was inconsistent.
What did Kelectron build?
Kelectron designed a hybrid Industrial IoT (IIoT) system using LoRaWAN for long-range data capture and Wi-Fi for high-throughput nodes like energy meters. Sensors: Vibration, temperature, humidity, current, voltage, RPM sensors; Connectivity: LoRaWAN (for remote, low-power devices), Wi-Fi (for real-time energy data); Gateways: Multichannel LoRa gateway + Raspberry Pi edge node; Backend: MQTT Broker + Python FastAPI + MongoDB; Dashboard: Custom web dashboard + charting + alert engine.
What results were achieved?
Reduced machine downtime by 22% in first 60 days Energy optimization strategies identified via load trend analytics Enabled predictive maintenance by correlating vibration + temperature spikes Fully wireless deployment reduced cabling cost by 40% Operators and managers now access data remotely via mobile dashboard
Which technologies were used?
MCUs: STM32, ESP32. Protocols: LoRaWAN, MQTT, HTTP. Connectivity: Wi-Fi, RS485, BLE (optional). Software: Python, FastAPI, MongoDB, Chart.js. Cloud/Local: Hybrid – edge analytics + optional cloud sync.
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