The Problem
MirrorIOT’s enterprise clients were accumulating technical debt at the infrastructure layer. IoT device fleets managed through major cloud provider platforms were increasingly locked into proprietary services — custom protocols, vendor-specific APIs, and managed services that made migration expensive and architecturally difficult. As the cost and capability landscape of cloud IoT services shifted, clients had no practical path to change providers without re-engineering their device integrations.
The hardware side created additional constraints. MirrorIOT’s deployments included lightweight edge devices — Raspberry Pi units and comparable single-board computers — that could not run heavy cloud SDKs or sustain high-bandwidth connections. The platform needed to operate efficiently on resource-constrained hardware while handling the data volumes generated by industrial sensor fleets.
The Constraints
True cloud agnosticism required protocol neutrality. Cloud-specific SDKs, even “abstracted” ones, introduce coupling that limits portability. The framework had to communicate through standard protocols that any cloud or on-premises environment could receive.
Multi-tenancy from the start. MirrorIOT serves enterprise clients who each manage their own device fleets. The platform needed tenant isolation at the data, configuration, and management layers — without requiring separate infrastructure deployments per client.
Security without performance compromise. Industrial IoT environments transmit operational data continuously. TLS/SSL overhead on lightweight devices is a real constraint; the authentication and encryption architecture had to be designed for the resource profile of the target hardware, not just for server-class deployments.
Our Approach
We built the platform in Golang, chosen for its performance characteristics on resource-constrained hardware and its suitability for high-concurrency network applications.
Device-to-platform communication uses MQTT for persistent, low-overhead messaging from devices to the broker layer. Data flows to the ingestion layer via gRPC streams — enabling high-throughput, bidirectional communication for devices that need to send sensor data continuously rather than in request-response cycles. Dynamic message schemas allow device payloads to evolve without requiring platform-side schema migrations.
The device registry supports multi-tenancy at the configuration level: each tenant manages their fleet independently with isolated data partitions and access controls, without requiring separate infrastructure.
OTA updates are managed through the platform — firmware and software packages are delivered securely to device fleets with rollback support. A built-in data retention and archival system manages the lifecycle of sensor data at scale, with configurable policies by tenant and data type.
The security architecture uses TLS/SSL mutual authentication at the device level — verified device identity before any data is accepted into the pipeline. Authentication is handled at the protocol layer rather than the application layer, keeping the computational overhead manageable on Raspberry Pi-class hardware.
Kubernetes manages container orchestration across cloud and on-premises deployment targets; RabbitMQ handles message queuing at the broker layer; InfluxDB stores time-series sensor data.
The Outcome
- Vendor independence achieved — clients can move between cloud providers or to on-premises without re-engineering device firmware or integration layers
- High-throughput ingestion handles industrial IoT data volumes with sub-second latency on the ingestion path
- Fleet management simplified — OTA updates, data retention policies, and device configuration managed from a single plane across multi-tenant deployments
- Security posture meets industrial standards — TLS/SSL mutual authentication with device-level verification
Client feedback: “The system helped us avoid cloud vendor lock-in.” — Joseph Baldwin, CTO, MirrorIOT
Team
Engagement: 5 months, 4 engineers (1 systems/embedded, 2 backend, 1 DevOps).
Stack: Golang, C++, gRPC, MQTT, GraphQL, RabbitMQ, PostgreSQL, InfluxDB, Docker, Kubernetes