Real-Time AI Monitoring System for 1 Lakh Smart LED Streetlights
Real-Time AI Monitoring System for 1 Lakh Smart LED Streetlights
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
SmartGrid·IoT · Energy & Infrastructure · IoT + AI
SmartGrid·IoT · Energy & Infrastructure · IoT + AI
SmartGrid·IoT · Energy & Infrastructure · IoT + AI
SmartGrid·IoT · Energy & Infrastructure · IoT + AI
Client
Leading Oil & Gas Company
Industry
TAGS
IoT · Edge AI · Infrastructure · Real-Time Monitoring
Technologies
6LoWPAN · IoT Gateway · Lamp Node Controllers · Cloud IoT Backend · Real-Time Analytics · Energy Metering · Python · AWS
Engagement
Forge™️ → Squad™️
Client Overview
A smart city infrastructure operator managing 1 Lakh (100,000) smart LED streetlights across a major urban road network. The client required real-time status monitoring, centralised configuration, energy utilisation tracking and fault detection — with absolute reliability. Failure of any unit incurred heavy penalties and required lane closures for maintenance.
Results:
100,000
streetlight units monitored in real-time
Zero
undetected failures post-deployment
100%
Implementation costs covered entirely by documented power savings and intelligent dimming
The Challenge
At 100,000 units across a live road network, traditional monitoring approaches couldn't scale. Any communication failure meant blind spots in fault detection. Undetected lamp failures triggered contractual penalties. Planned maintenance required stopping traffic in active lanes — expensive and disruptive. The client needed a system that could pinpoint malfunctioning units instantly, enable remote configuration without on-site visits, and make energy savings pay for the implementation cost.
The Solution
Palpx designed a 6LoWPAN-based low-power, highly reliable mesh communication layer connecting all 100,000 units to a centralised IoT cloud backend. The cloud platform provided real-time status dashboards, energy utilisation monitoring with built-in energy metering, predictive fault alerts and remote configuration/control for individual or grouped units. AI analytics layer identified failure patterns to enable preventive maintenance scheduling — reducing unplanned outages and lane closure events. Power savings from intelligent dimming and fault prevention were documented to demonstrate ROI against implementation cost.
Client
Leading Oil & Gas Company
Industry
TAGS
IoT · Edge AI · Infrastructure · Real-Time Monitoring
Technologies
6LoWPAN · IoT Gateway · Lamp Node Controllers · Cloud IoT Backend · Real-Time Analytics · Energy Metering · Python · AWS
Engagement
Forge™️ → Squad™️
Client Overview
A smart city infrastructure operator managing 1 Lakh (100,000) smart LED streetlights across a major urban road network. The client required real-time status monitoring, centralised configuration, energy utilisation tracking and fault detection — with absolute reliability. Failure of any unit incurred heavy penalties and required lane closures for maintenance.
Results:
100,000
streetlight units monitored in real-time
Zero
undetected failures post-deployment
100%
Implementation costs covered entirely by documented power savings and intelligent dimming
The Challenges
At 100,000 units across a live road network, traditional monitoring approaches couldn't scale. Any communication failure meant blind spots in fault detection. Undetected lamp failures triggered contractual penalties. Planned maintenance required stopping traffic in active lanes — expensive and disruptive. The client needed a system that could pinpoint malfunctioning units instantly, enable remote configuration without on-site visits, and make energy savings pay for the implementation cost.
The Solution
Palpx designed a 6LoWPAN-based low-power, highly reliable mesh communication layer connecting all 100,000 units to a centralised IoT cloud backend. The cloud platform provided real-time status dashboards, energy utilisation monitoring with built-in energy metering, predictive fault alerts and remote configuration/control for individual or grouped units. AI analytics layer identified failure patterns to enable preventive maintenance scheduling — reducing unplanned outages and lane closure events. Power savings from intelligent dimming and fault prevention were documented to demonstrate ROI against implementation cost.
SmartGrid·IoT · Energy & Infrastructure · IoT + AI
Client
Leading Oil & Gas Company
Industry
TAGS
IoT · Edge AI · Infrastructure · Real-Time Monitoring
Technologies
6LoWPAN · IoT Gateway · Lamp Node Controllers · Cloud IoT Backend · Real-Time Analytics · Energy Metering · Python · AWS
Engagement
Forge™️ → Squad™️
Client Overview
A smart city infrastructure operator managing 1 Lakh (100,000) smart LED streetlights across a major urban road network. The client required real-time status monitoring, centralised configuration, energy utilisation tracking and fault detection — with absolute reliability. Failure of any unit incurred heavy penalties and required lane closures for maintenance.
Results:
100,000
streetlight units monitored in real-time
Zero
undetected failures post-deployment
100%
Implementation costs covered entirely by documented power savings and intelligent dimming
The Challenges
At 100,000 units across a live road network, traditional monitoring approaches couldn't scale. Any communication failure meant blind spots in fault detection. Undetected lamp failures triggered contractual penalties. Planned maintenance required stopping traffic in active lanes — expensive and disruptive. The client needed a system that could pinpoint malfunctioning units instantly, enable remote configuration without on-site visits, and make energy savings pay for the implementation cost.
The Solution
Palpx designed a 6LoWPAN-based low-power, highly reliable mesh communication layer connecting all 100,000 units to a centralised IoT cloud backend. The cloud platform provided real-time status dashboards, energy utilisation monitoring with built-in energy metering, predictive fault alerts and remote configuration/control for individual or grouped units. AI analytics layer identified failure patterns to enable preventive maintenance scheduling — reducing unplanned outages and lane closure events. Power savings from intelligent dimming and fault prevention were documented to demonstrate ROI against implementation cost.