CASE STUDY // Cloud · IoT
IoT SmartParking System
Cloud-hosted IoT simulation for smart parking with real-time sensor monitoring and booking workflows.
- Role
- Designed the cloud infrastructure and the sensor-to-dashboard data flow.
- Context
- Personal cloud/IoT project; sensors are simulated rather than physical hardware.
- System Type
- IoT / Real-Time Monitoring System
- Status
- Documented
- Priority
- Primary
- Stack
- AWS EC2 · Elastic Load Balancer · MQTT · Node-RED · MongoDB
Problem
Modeling a real-time IoT system for parking availability required simulated sensor input, message routing, and a booking workflow layered on scalable cloud infrastructure.
Context
Personal cloud/IoT project; sensors are simulated rather than physical hardware.
Role & Ownership
Designed the cloud infrastructure and the sensor-to-dashboard data flow.
SCOPE CLARIFICATION
This system uses simulated sensor data rather than physical deployed parking hardware. It demonstrates real-time data flow, MQTT messaging, Node-RED orchestration, AWS-hosted services, and MongoDB-backed monitoring workflows — sensors are simulated, not physical hardware.SYSTEM DESIGN
Architecture
Simulated sensors publish over MQTT, orchestrated through Node-RED flows, into a MongoDB-backed monitoring dashboard and booking workflow. Infrastructure runs on AWS EC2 with AMI replication and an Elastic Load Balancer.
SCOPE NOTE
Sensors are simulated, not physical hardware.
KEY CAPABILITIES
What it does
Simulated sensors publishing over MQTT
Node-RED flow orchestration
Real-time monitoring dashboard
Booking workflows
Load-balanced, AMI-replicated EC2 infrastructure
TECHNICAL STACK
Technologies used
CAPABILITY PROOF
What this proves
End-to-end cloud infrastructure design (load balancing, AMI replication) combined with IoT protocol handling — a cloud-infrastructure signal distinct from the Kubernetes labs.
CAPABILITY DOMAINS EVIDENCED
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CAPABILITY LAYER
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