Time-Series, Real-Time, Secure: COTS Device Data Management for IoT & AI

In the defense industry, pairing Commercial Off-the-Shelf (COTS) hardware with embedded software speeds fielding and reduces costs while aligning with open, modular standards and goals. Success hinges on rigor: ruggedization, deterministic real-time behavior, long-life support amid silicon churn, and built-in cybersecurity and safety. New technologies for artificial intelligence and the Internet of Things (IoT) must be thoroughly vetted and isolated in defense applications. With disciplined systems engineering and DevSecOps, COTS solutions deliver faster, easier tech refreshes and ensure compliance across decades of upgrades.

In the embedded world, data management often defines success or failure. As manufacturers integrate sensors, microcontrollers (MCUs), microprocessors (MPUs), neural processing units (NPUs), and electronic control units (ECUs) into connected products, they face one consistent challenge: managing and understanding the growing flow of device data. Choosing the right database technology to manage data has become just as critical as selecting the right hardware. Too often, developers adopt open-source database products because they’re “free,” only to discover later that such solutions lack determinism, reliability, and long-term maintainability, leading to missed deadlines and expensive redesigns.

In embedded systems, adopting a COTS embedded database means selecting a commercially supported, drop-in data engine rather than building custom storage from scratch. When targeting MCU and MPU-based devices, this database must deliver deterministic latency for ingestion and queries, operate within tight RAM and flash budgets, and ensure power-fail safety through atomic commits, journaling, or copy-on-write mechanisms. Database storage should be flash-aware, minimizing write amplification and extending the life of storage media. Built-in security features like data encryption and authentication are essential for protecting operational data, while native time-series and indexing support enable real-time insights directly on the device. Equally important, selective synchronization ensures that only meaningful summaries or events traverse limited links to gateways or the cloud. Over time, schema evolution, OTA-friendly upgrades, and long-term vendor support keep products stable and adaptable throughout their lifecycle.

A COTS embedded database provides a complete, ready-to-integrate data management foundation that spans the full embedded ecosystem, from MCUs to MPUs and ECUs. On microcontrollers, it enables deterministic data ingestion, time-series capture, and real-time decision-making within constrained resources. On microprocessors, it supports high-performance analytics, AI inference integration, and multi-threaded processing for edge and gateway applications. In electronic control units (ECUs), it ensures power-fail-safe, secure storage of operational and diagnostic data, vital for automotive, industrial, and transportation systems. When paired with an observability platform, engineers can visualize trends, drift, and anomalies in real time. At the same time, an integrated data distribution layer manages selective synchronization between nodes, gateways, and cloud environments. This unified approach empowers manufacturers to deploy scalable, secure, and maintainable embedded systems that seamlessly collect, process, observe, and distribute data across the entire device network.

Seven Critical Requirements for Embedded Database Success

1. Deterministic Performance
2. Embedded systems must operate within strict timing constraints. A robust database must guarantee bounded latency for ingestion and queries, avoiding unpredictable garbage collection or cache spikes, to ensure reliable real-time behavior under multitasking or real-time operating system (RTOS) conditions.
3. Data Durability & Power-Fail Safety
4. Industrial and mission-critical systems must retain data through resets, brownouts, and software crashes. The database must employ atomic commits, journaling, or copy-on-write to ensure data integrity, even on flash or SSD-based storage.
5. Resource Efficiency & Scalability
6. Although COTS platforms often have more resources than MCUs, they still run multiple workloads, control logic, AI inference, networking, and analytics. The database must remain lightweight, efficient, and modular, scaling from kilobytes to gigabytes without consuming excessive CPU or memory.
7. Security & Access Control
8. With increasing connectivity, data security is non-negotiable. Embedded databases must include encryption and authentication, aligning with industrial cybersecurity standards such as IEC 62443 to ensure end-to-end protection of sensitive operational data.
9. Data Distribution & Synchronization
10. Modern edge systems are inherently distributed. The database should support selective synchronization, replication, and data filtering, so only relevant, compressed, or aggregated data flows between devices and gateways, reducing bandwidth usage and improving responsiveness.
11. Observability & Analytics Integration
12. Visibility is key to performance optimization and AI enablement. The database must provide time-series management, real-time dashboards, and hooks for analytics and ML frameworks, enabling local monitoring, predictive maintenance, and continuous improvement at the device level.
13. Maintainability & Long-Term Evolvability
14. Embedded systems often remain in the field for a decade or more. The database should support schema evolution, version upgrades, and remote lifecycle management without downtime or complex data migrations, ensuring the system evolves alongside firmware and AI updates.

Why ITTIA DB Platform Is the Ideal COTS Candidate

The ITTIA DB Platform brings these seven essentials together into one cohesive solution. It provides deterministic performance, power-fail safety, and security-by-design for MCU (ITTIA DB Lite), MPU (ITTIA DB), and ECU (ITTIA DB Lite or ITTIA DB) class systems. Developers gain a complete data management stack, from local time-series ingestion and AI-ready storage to selective synchronization, observability dashboards (ITTIA Analitica), and long-term maintainability. With unified components for real-time operation, analytics, data distribution (ITTIA Data Connect), and visualization, it enables manufacturers to build edge devices that are flash-safe, real-time, and secure, ready to support IoT and AI applications for years to come.