How Namla Edge AI helped a Middle Eastern government deploy and orchestrate thousands of NVIDIA Jetson devices for intelligent road surveillance—while conquering extreme heat challenges.

The Challenge

A major Middle Eastern government agency needed to deploy an intelligent road surveillance network spanning thousands of kilometers of highway. The vision: AI-powered cameras capable of vehicle counting, license plate recognition, and traffic violation detection—including speeding, dangerous overtaking, and continuous line crossing.

⚠️ The Heat Factor

Middle Eastern summers present a unique operational challenge. Ambient temperatures regularly exceed 45-50°C, and roadside cabinets housing the NVIDIA Jetson devices can experience even higher internal temperatures. This thermal stress leads to performance degradation, system throttling, and unexpected reboots—potentially compromising public safety when surveillance goes offline.

The client needed a solution that could not only deploy and manage AI models at scale but also provide real-time visibility into environmental conditions affecting their edge infrastructure.

The Namla Solution

We deployed a comprehensive edge AI platform that addresses both the orchestration challenge and the environmental monitoring needs. Our solution combines GitOps-driven continuous deployment, secure SD-WAN connectivity, and real-time telemetry collection into a unified platform.

Core Capabilities

AI-Powered Traffic Intelligence

Each NVIDIA Jetson edge node runs sophisticated computer vision models optimized for real-time traffic analysis:

Cabinet Environmental Monitoring

To address the thermal challenges unique to desert deployments, we implemented a comprehensive environmental monitoring system. Modbus-connected sensors inside each roadside cabinet feed real-time data through our telemetry pipeline.

Data Flow Architecture

Sensor → Modbus TCP → MQTT Bridge → SD-WAN → Central MQTT → Telegraf → InfluxDB → Grafana

Technology Stack

NVIDIA Jetson · Modbus TCP · Mosquitto MQTT · Telegraf · InfluxDB · Grafana · Cloudflared · Namla SD-WAN

Edge Components

Mosquitto DaemonSet deploys an MQTT broker bridge on every edge node, listening on port 1883 and exposed via NodePort 30083. The Modbus to MQTT Exporter reads temperature and humidity registers from physical sensors using modpoll, publishing structured JSON to topic-per-node:

// Topic: edge-name/<NODE_NAME>/modbus
{
  "humidity": 66.7,
  "temperature_celsius": 24.7,
  "temperature_fahrenheit": 76.5
}

For nodes without physical sensors, an MQTT Producer DaemonSet provides simulated data for development and testing scenarios.

Central Infrastructure

All edge brokers bridge to the Central Mosquitto Broker over Namla's secure SD-WAN. Telegraf subscribes to the edge-name/# wildcard topic and writes metrics to InfluxDB for time-series storage and historical analysis.

Grafana provides comprehensive visualization including:

• Real-time gauges for current readings
• Historical trend analysis
• Threshold-based alerting
• Geographic heatmaps across Saudi Arabia
• Per-edge drill-down views

🌐 Secure Dashboard Access

To provide safe external access, the Grafana dashboard is exposed through a Cloudflared reverse tunnel

Results & Impact

The combined edge AI and environmental monitoring platform has transformed the client's road safety operations:

Proactive temperature monitoring enables maintenance teams to address thermal issues before they impact AI performance. GitOps-driven deployment means enhanced models and bug fixes reach all 1000+ locations within hours, not weeks. And zero-touch provisioning has reduced new site deployment time from days to under an hour.

Ready to Scale Your Edge AI?

Whether you're deploying smart city infrastructure, industrial IoT, or intelligent transportation systems, Namla provides the orchestration platform to manage edge AI at any scale.

Get in Touch →