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The Challenge: Real-Time IoT Micro-mobility Dispatching

RideZib operated a fast-growing fleet of 10,000 electric scooters and bikes across European cities but faced critical dispatch issues: GPS drifts caused users to search endlessly for vehicles, IoT lock commands lagged up to 15 seconds, and battery status metrics were inconsistent. They needed an intelligent, real-time micro-mobility dispatch and booking platform with a cross-platform mobile app and a high-performance dispatch dashboard.

Technical hurdles included managing real-time MQTT message queues from 10,000 active scooters, building a low-latency geo-spatial indexing database to show near vehicles instantly, and designing a cross-platform mobile application that renders smooth map movements and handles biometric locks in seconds.

  • Connecting AWS IoT Core to manage real-time MQTT state messages from the physical scooter fleet.
  • Utilizing Redis Geospatial indexing to serve "find near scooters" queries under 10ms.
  • Developing a responsive, single-codebase Flutter app for rapid iOS & Android updates.
  • Implementing localized Bluetooth backup lock protocols for areas with poor cell coverage.
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Solution & Architecture

We engineered a serverless real-time mobility backend using FastAPI and AWS IoT Core. Scooters publish their telemetry (GPS, battery, locking state) via MQTT to AWS IoT Core, which routes messages to a fast Redis cluster. By using Redis GEOADD/GEORADIUS operations, the backend resolves the "nearest 20 vehicles" query for the mobile app in under 8ms.

The user-facing mobile application was built using Flutter. We designed custom map integrations that stream vehicle updates smoothly. To address connectivity drops in concrete parkades, we integrated a fallback BLE (Bluetooth Low Energy) lock protocol: if the mobile app cannot reach the server, it securely negotiates a direct cryptographic lock handshake with the scooter via Bluetooth.

For city fleet dispatchers, we built a React dashboard that maps fleet distribution, heatmaps of ride demand, and active geofences. If scooters are parked outside approved zones, the server automatically fires notifications to local maintenance dispatchers, optimizing fleet positioning.

The Flutter application supports native Google Pay, Apple Pay, and local European payment methods, integrated directly with Stripe. The infrastructure is managed via Terraform and deployed on serverless AWS Lambda clusters, ensuring that RideZib pays only for CPU seconds used during active booking surges.

Solution & Architecture

We engineered a serverless real-time mobility backend using FastAPI and AWS IoT Core. Scooters publish their telemetry (GPS, battery, locking state) via MQTT to AWS IoT Core, which routes messages to a fast Redis cluster. By using Redis GEOADD/GEORADIUS operations, the backend resolves the "nearest 20 vehicles" query for the mobile app in under 8ms.

The user-facing mobile application was built using Flutter. We designed custom map integrations that stream vehicle updates smoothly. To address connectivity drops in concrete parkades, we integrated a fallback BLE (Bluetooth Low Energy) lock protocol: if the mobile app cannot reach the server, it securely negotiates a direct cryptographic lock handshake with the scooter via Bluetooth.

For city fleet dispatchers, we built a React dashboard that maps fleet distribution, heatmaps of ride demand, and active geofences. If scooters are parked outside approved zones, the server automatically fires notifications to local maintenance dispatchers, optimizing fleet positioning.

The Flutter application supports native Google Pay, Apple Pay, and local European payment methods, integrated directly with Stripe. The infrastructure is managed via Terraform and deployed on serverless AWS Lambda clusters, ensuring that RideZib pays only for CPU seconds used during active booking surges.

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