In embedded systems especially those used in automotive, military, and industrial applications secure random number generation is a foundational element of data security. Whether you're implementing encryption protocols like AES, verifying data integrity with HMAC, or protecting against replay attacks with nonces, the ability to generate unpredictable, unique numbers is essential.

Yet many microcontrollers and real-time systems lack a hardware-based random number generator (TRNG). That’s why Simma Software engineers have developed a robust and proven approach to generating 256-bit random numbers using a blend of entropy sources and cryptographic hashing. This method is optimized for embedded platforms where security, determinism, and low overhead are key.

Understanding Nonces: Unique by Design

A nonce (pronounced nons) is a “number used once.” In cryptography, it’s used to introduce uniqueness to a transaction or cryptographic operation to ensure it cannot be repeated or replayed. Nonces don’t need to be random—but they do need to be guaranteed unique.

In embedded applications, nonces can be derived from values like:

• CPU reset count
• Uptime or tick count
• Total number of random values generated

By combining these with other system-specific data, we create values that are unique per session, per device, or even per transaction—ensuring resilience against attacks such as message replay or duplicated command injection.

Why Random Number Generation Matters

Random numbers are used in:

• Session keys for encrypted communication
• Nonces for request validation and signature freshness
• Initialization vectors (IVs) for block cipher modes
• Challenge-response protocols
• Secure bootloaders that authenticate firmware updates

Without proper randomness, even strong cryptographic algorithms like AES and SHA can be compromised. Attackers often exploit weaknesses in RNGs to predict keys or recreate security tokens.

That’s why Simma Software’s approach focuses on mixing entropy and contextual data to create robust, device-specific randomness—even in environments without dedicated RNG peripherals.

The Simma Software Approach to Randomness

The challenge in embedded systems is obtaining sufficient entropy. So rather than relying on a single source, our strategy collects a pool of diverse, semi-random, and unique values. These inputs fall into three categories:

1. Entropy Sources

These are unpredictable or noisy measurements that vary from boot to boot or system to system.

• Uptime in milliseconds – Varies naturally due to boot timing and external factors.
• High-speed freerunning timer – Offers precise, rapidly changing values.
• CPU temperature – Changes dynamically with load and ambient conditions.
• Supply voltage (in mV) – Reflects battery or power fluctuations.
• Floating analog pin readings – Unused ADC pins act like open antennas, picking up environmental noise.
• GPS location (if available) – Adds real-world, ever-changing data.
• Fuel level, battery charge, or engine RPM – Application-specific values useful in automotive systems.
• Wireless signal strength (RSSI) – Varies based on environmental and RF conditions.

2. Contextual Diversification Inputs

These are values that are unique per product or per deployment, ensuring one system’s random number output differs from another’s.

• Vehicle VIN
• MAC address
• CPU ID
• Date of manufacture
• Software version
• Hardware version

3. Nonce-like Counters and State Data

These ensure freshness over time, helping avoid duplication of generated numbers.

• CPU reset count
• Total number of random numbers generated
• Number of network packets sent/received

Bringing It All Together with SHA-256

Once 16 to 32 of the above values are collected, they’re combined into a single buffer and passed through a SHA-256 hashing function. The output is a 256-bit cryptographic digest—a secure, statistically uniform random number.

This method doesn’t rely on any one source being perfect. The combination of noise, system-specific constants, and event counters ensures high entropy and uniqueness. The use of SHA-256 ensures the final result cannot be reverse-engineered or predicted from any subset of input data.

In practice:

[entropy + context + nonces] → SHA-256 → 256-bit random number

This approach is efficient, portable, and scalable. It also aligns with FIPS and NIST guidance on deterministic random bit generation (DRBG), where entropy collection is followed by a strong hash function.

Benefits of Simma’s RNG Strategy

• Cryptographic strength even on microcontrollers without hardware RNG
• System-specific uniqueness to avoid collisions
• Forward and backward secrecy via hash-based one-way generation
• Low overhead for use in real-time embedded applications
• Simple implementation using existing cryptographic libraries

Conclusion

Secure systems begin with secure randomness. At Simma Software, we’ve built a field-proven method for generating cryptographically secure 256-bit random numbers in embedded systems without relying on TRNGs. By combining entropy, nonces, and contextual product data, our RNG strategy empowers systems to resist prediction, spoofing, and replay attacks.

If you're building secure bootloaders, communication stacks, or diagnostic tools that require rock-solid randomness, our team can help. Contact Us to learn more about Simma Software’s embedded cryptographic libraries and secure design practices.