Secure Vehicle-to-Cloud Telematics Architecture for Real-Time Fleet Monitoring and Predictive Maintenance
Year: 2026
Type: article
Abstract: Connected vehicle platforms have become central to intelligent transportation systems, linking in-vehicle telematics hardware to cloud analytics through secure, low-latency data paths. This paper presents a layered connected vehicle architecture that integrates an on-board DataLogger, Bluetooth Low Energy (BLE) short-range links, a mobile software development kit (SDK), and a cloud gateway to deliver continuous real-time vehicle data acquisition for fleet monitoring. The vehicle layer acquires sensor, GPS, and on-board diagnostic (OBD-II) data across CAN, LIN, FlexRay, and Automotive Ethernet buses. The mobile SDK layer performs device discovery, geolocation-triggered auto-connect, session management, and buffered upload, while the gateway layer carries out device authentication, integrity verification, and Firmware Over-the-Air (FOTA) distribution before forwarding validated telemetry to cloud storage and analytics services. Security is enforced end-to-end through OAuth 2.0 consent flows, TLS-encrypted transport, API-key authorisation, and device attestation, mitigating spoofing, replay, and man-in-the-middle threats while supporting GDPR and CCPA compliance. The architecture is analysed against established mobile application performance benchmarks covering launch latency, API response time, crash rate, and onboarding duration, showing that connection-establishment delay beyond three seconds materially degrades retention in fleet-facing applications. Design trade-offs in cross-platform support, intermittent connectivity, data synchronisation, certification cost, and lifecycle maintenance are discussed. The resulting platform provides a modular, standards-aligned foundation for predictive maintenance, usage-based insurance, driver behaviour analytics, and emerging Vehicle-to-Everything (V2X), electric vehicle, and autonomous mobility deployments
Author: Tharun K Nallamothu
Institution: Microsoft (United States)
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203462658
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