In-depth analysis of our IoT integration architecture.
Technical Overview
User Device (Embedded System)
Our embedded systems are equipped with microcontrollers capable of running real-time operating systems (RTOS) for efficient task management. They utilize low-power wireless protocols like BLE 5.0 for extended battery life and increased range. For direct connections, we've implemented USB 3.1 for high-speed data transfer.
Mobile Client App
Developed using cross-platform frameworks like React Native or Flutter, our mobile app ensures consistent performance across iOS and Android. It implements robust BLE libraries for reliable device communication and uses secure WebSocket connections for real-time updates from our backend.
Web App
Our web application is built on a modern JavaScript framework such as React or Vue.js, utilizing server-side rendering for improved performance. We've implemented WebSocket connections for real-time updates and progressive web app (PWA) capabilities for offline functionality.
API Gateway
We've deployed a robust API Gateway using technologies like Kong or AWS API Gateway. It implements JWT authentication, rate limiting, and request/response transformation. The gateway also handles protocol translation, allowing our backend to communicate via gRPC for improved performance while maintaining REST compatibility for clients.
Device API, Mobile API, Web API
These microservices are containerized using Docker and orchestrated with Kubernetes for scalability and easy deployment. They're written in high-performance languages like Go or Rust, optimized for concurrent operations. Each service implements circuit breakers and retries for improved resilience.
Message Queue (SQ)
We utilize Apache Kafka for our message queue, leveraging its high throughput and fault-tolerance. Our implementation includes multiple topics for different types of messages, with consumer groups to ensure parallel processing and exactly-once delivery semantics.
Databases
Our primary database is MongoDB, chosen for its flexibility with IoT data models and horizontal scalability. We've implemented sharding for improved read/write performance and have set up replica sets for high availability. Time-series data is stored in InfluxDB for efficient querying of sensor data over time
External Services
Redis: We use Redis Cluster for distributed caching, improving read performance for frequently accessed data. It's also utilized for distributed locking in our microservices architecture.
Amazon S3: Our S3 implementation includes lifecycle policies for cost-effective long-term storage. We use server-side encryption for data at rest and implement versioning for easy rollbacks.
Kafka: Beyond message queuing, we leverage Kafka Streams for real-time data processing, allowing us to perform complex analytics on IoT data streams.
