Solar Panel Sensor-Based True Random Number Generation
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Solution Overview
Problem
Conventional random number generators used in secure communication systems, such as those in remote sensor networks and UAVs, often produce pseudo-random numbers, making them susceptible to hacking, and existing truly random number generators are complex and costly, requiring dedicated circuitry that is not feasible in resource-constrained systems.
Innovation Solution
A random number generation system that leverages the varying output characteristics of a solar power system, using sensors to measure parameters like voltage or current from solar panels to generate truly random numbers, thereby eliminating the need for complex circuitry and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional random number generators are used, then the system is simple and low-cost, but the generated numbers are pseudo-random and susceptible to hacking
Solution Approach 1:
The solar power system's existing circuitry serves dual purposes: generating power and providing random number generation. The inherent variability in solar panel output, caused by environmental factors like sunlight intensity and temperature, is directly utilized as the random seed source, eliminating the need for dedicated random number generation hardware.
Solution Approach 2:
The invention exploits changes in the electrical parameters (voltage, current) of the solar power system that occur naturally due to environmental variations. These parameter fluctuations, which are normally considered noise or inefficiency in power generation, are captured and transformed into high-entropy random numbers through hashing functions.
2Reliability
If dedicated random number generation circuitry is added, then truly random numbers can be generated, but the system complexity and cost increase
Solution Approach 1:
The solar power system's electrical circuits perform multiple functions: power generation, power regulation, and random number generation. The existing voltage regulator and other power management components are repurposed to harvest entropy from the system's natural electrical variations, eliminating the need for separate random number generation hardware.
Solution Approach 2:
A software-based entropy harvesting mechanism acts as an intermediary between the solar power system's electrical output and the random number generation process. The system captures electrical parameters through existing sensors or measurements, processes them through hashing functions, and generates cryptographically secure random numbers without requiring direct hardware modification.
3Reliability
If complex random number generators are deployed, then security is improved, but space and resource requirements increase
Solution Approach 1:
The solar power system provides its own entropy source through its inherent electrical variability. By monitoring the system's own power generation characteristics, the invention creates a self-contained random number generation solution that requires no additional physical space or external entropy sources.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a cost-effective and space-efficient method to generate truly random numbers, enhancing the security of communication systems by utilizing existing solar power system circuitry, reducing the risk of hacking and simplifying system design.
Implementation Method 1
a solar power system has at least one solar power panel configured to convert solar power into electrical power
Data Source
AI summary
An exemplary random number generation system leverages the r includes at least one solar power panel of a solar power system, at least one sensor and a random number generator. The sensor senses one or more output parameters (e.g., voltage or current) from the solar power system and provides the sensed parameter to the random number generator, which uses the sensed parameter to generate a number that is truly random (i.e., is not deterministic). As an example, the random number generator may receive multiple samples of the measured parameter and generate a random number based on a difference of the multiple samples. If desired, the random number generator may include an algorithm to remove biasing in the random number.