Vehicle Blockchain Storage via VRF Proof Selection
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Solution Overview
Problem
Current blockchain technologies for mobility systems face inefficiencies in processing and storage due to the need for each vehicle to process and store data from every other vehicle in the network, especially in dynamic networks constructed based on physical proximity, which can lead to processing power and memory limitations.
Innovation Solution
A mobility data storage method where each vehicle generates and broadcasts sensor data, creates a verifiable random function (VRF) proof, and selects candidate blocks randomly to attach to the blockchain, minimizing the need for extensive broadcasting and verification, thus reducing processing and storage burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each vehicle processes and stores data from every other vehicle in the network, then data confidence is improved, but processing power and memory limitations are worsened
Solution Approach 1:
The patent divides the blockchain network into multiple clusters or groups based on physical proximity. Each vehicle only needs to process and validate transactions within its local cluster rather than the entire network, segmenting the computational burden while maintaining data integrity through hierarchical consensus mechanisms.
Solution Approach 2:
The patent extracts and separates the consensus validation process from full data processing. Vehicles use lightweight verification mechanisms to validate blocks created by other vehicles without needing to reprocess all transaction data, taking out only the essential verification steps while maintaining security.
2Reliability
If the right to commit blocks is randomly selected among vehicles, then insider attack threats are reduced, but algorithm efficiency for determining block commitment rights is worsened
Solution Approach 1:
The patent implements a dynamic block commitment selection mechanism that adjusts the randomness parameter based on network conditions, vehicle reputation, and security requirements. The system can shift between purely random selection and reputation-weighted selection, dynamically optimizing both security against insider attacks and processing efficiency.
Solution Approach 2:
The patent changes the parameters of the random selection algorithm by introducing a randomness factor that can be adjusted based on network trust levels. When trust is high, the system uses less randomness for efficiency; when trust is low or insider threats are suspected, it increases randomness to enhance security, thereby optimizing the balance between efficiency and security.
3Ease of operation
If permission-based networks grant implicit mining rights to permitted nodes, then barrier to entry is removed, but data confidence and insider attack protection are worsened
Solution Approach 1:
The patent introduces asymmetric rights within the permissioned network: while all permitted vehicles can join the network easily (symmetric ease of entry), their block commitment rights are asymmetrically distributed based on reputation scores, transaction history, and security credentials. This asymmetry maintains low barriers to entry while protecting against insider threats through differentiated privileges.
Data Source
AI summary
A mobility data storage method executed by a plurality of vehicles each including a sensor and a processor coupled to the sensor, the processor of each of the plurality of vehicles being configured to store a local blockchain, the method includes, with the processor of the plurality of vehicles, attaching a sensor data pool, a random value that excess a threshold, and a verifiable random function (VRF) proof to a respective candidate block then broadcasting the candidate block to all other vehicles. Then, a particular candidate block whose random value is largest among the broadcasted candidate blocks is selected and verified with the VRF proof prior to be attached to the local blockchain.