# A Governance-Aware Private Cloud Architecture for Scalable Multi-Provider Vehicle-Based Multimodal Sensing

## TL;DR

This paper presents a private cloud architecture for vehicle-based sensing that ensures privacy, scalability, and governance across multiple providers.

## Contribution

The architecture integrates governance, privacy, and provider isolation as core design principles with ingestion-time visual abstraction.

## Key findings

- The system achieved real-time visual inference with ~200 ms per frame.
- Provider-level isolation was maintained under concurrent access.
- Metadata abstraction reduced storage needs by up to 95%.

## Abstract

Vehicle-mounted sensing enables high-resolution urban monitoring but remains constrained by heterogeneous multimodal integration, intermittent connectivity, privacy-sensitive visual data, and the absence of enforceable multi-provider governance. This paper introduces a governance-aware private cloud architecture that treats provider isolation, role-based access control, and privacy-by-design as core architectural properties rather than application-layer add-ons. The layered, containerised microservice design supports asynchronous store-and-forward ingestion, modality-specific processing pipelines, and GPU-accelerated object detection for structured metadata extraction. A key innovation is ingestion-time visual abstraction, which structurally separates raw imagery from derived observations and enforces lifecycle-based retention policies, embedding data minimisation directly into the data flow. The fully open-source implementation is validated through a two-month multi-provider pilot with continuous multimodal collection. Results demonstrate stable ingestion without data loss, real-time visual inference (~200 ms per frame), strict provider-level isolation under concurrent access, and up to 95% storage reduction via metadata abstraction. The findings establish a replicable architectural paradigm for scalable, privacy-aware, multi-actor mobile sensing infrastructures suitable for metropolitan-scale smart city deployment.