When organizations begin scaling connected vehicle platforms, the focus is usually on features such as real time telemetry, battery monitoring, predictive maintenance, driver analytics, and geofencing. What often gets overlooked is the operational cost of processing telemetry at scale.
Recently, our team worked with a fast-growing EV-focused enterprise planning a large-scale connected mobility rollout. While the architecture met technical requirements, modeling expected telemetry volumes revealed that costs would rise significantly as the fleet grew, making cost optimization essential.
This article shares practical lessons from that engagement, including architectural decisions, trade offs, and optimization techniques that helped create a scalable and commercially sustainable Internet of Things (IoT) platform.
What Makes This Approach Effective?
- Real world architectural insights derived from an enterprise connected mobility engagement.
- Practical AWS cost optimization strategies for high volume telemetry workloads.
- Clear explanation of hot path and cold path data architectures.
- Design considerations for balancing real time performance with operational costs.
- Security, compliance, and scalability best practices for production deployments.
Understanding the AWS Architecture Behind the Platform
The proposed AWS architecture was designed to process telemetry data based on its business value rather than treating every message identically. Vehicle telemetry (Battery health, Battery SoC, Tyre Pressure, Accelerometer, Speed Sensor etc) is securely ingested through AWS IoT Core, with AWS IoT Defender providing continuous device monitoring and security validation.
Amazon Kinesis Data Streams serves as the central ingestion layer, enabling telemetry to be distributed to multiple downstream systems without duplicating data pipelines. Real-time operational data, such as battery status, vehicle location, and safety alerts, flows through AWS Lambda into TimeseriesDB, providing low-latency access for dashboards and operational workloads.
For analytics and long-term retention, telemetry is routed through Amazon Kinesis Data Firehose, converted into Parquet format, and stored in Amazon S3. This significantly reduces storage and query costs while supporting reporting, analytics, and machine learning use cases.
Application services run on Amazon ECS Fargate, while Route 53, CloudFront, AWS WAF, and Application Load Balancer provide secure and scalable access to end users. Security across the platform is enforced through IAM, KMS, CloudTrail, GuardDuty, and Secrets Manager, ensuring encryption, auditing, and least-privilege access controls.
A hot path architecture was used for safety critical and operational telemetry such as vehicle location, battery status, and alerts. These workloads flowed through Amazon Kinesis Data Streams, AWS Lambda, and TimescaleDB to provide near real time visibility.
For historical analytics, diagnostics, and reporting, data was routed through Amazon Kinesis Firehose and stored in Amazon S3 using Parquet format. This significantly reduced analytics costs while improving query efficiency.
The result is a scalable, secure, and cost-efficient foundation capable of supporting both current telemetry workloads and future connected mobility services.
One of the most important observations from the project was that not all telemetry data deserves the same treatment. Many organizations initially design their systems so that every sensor reading follows the same ingestion, processing, and storage path. While simple to implement, this approach can significantly increase operational costs.
The optimization strategy focused on three key areas:
• Topic Consolidation: Reducing unnecessary MQTT topic complexity and rule evaluations.
• Payload Right Sizing: Minimizing message size to reduce ingestion, processing, and storage costs.
• Tiered Storage Architecture: Separating operational real time workloads from analytical workloads.
Device-Aware Telemetry Optimization
While telemetry optimization was a major focus of this initiative, connected mobility platforms involve far more than vehicle data ingestion. Modern EV ecosystems rely on mobile applications, fleet management dashboards, predictive maintenance systems, customer notifications, OTA updates, analytics platforms, and third-party integrations.
A cost-efficient telemetry architecture provides a strong foundation for these capabilities. By ensuring data is ingested, processed, and stored efficiently, the platform supports ongoing cloud cost optimization while enabling future innovations such as battery performance analytics, AI-driven maintenance recommendations, and fleet optimization without requiring a complete redesign of the underlying data platform.
Key Outcomes
• Reduced unnecessary telemetry processing overhead
• Improved long term storage efficiency through Parquet conversion
• Optimized database sizing based on actual usage patterns
• Enhanced scalability for future fleet growth
• Maintained enterprise grade security and compliance controls
Author's Perspective
The most valuable takeaway from this engagement was that successful IoT (Internet of Things) architectures are not defined solely by technology choices. They are shaped by a clear understanding of business requirements, realistic data freshness expectations, disciplined cloud cost optimization, and effective cost governance from day one.
The blog is written by Vedant Sakinal (Junior Cloud Consultant @ Cloud.in)
No comments:
Post a Comment