Decentralized Vehicle IoT Data Exchange System
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Solution Overview
Problem
Centralized systems for collecting and analyzing real-time data from vehicles are inflexible and prone to bottlenecks, limiting the ability for vehicles to efficiently share critical data and update their operations based on data patterns and trends.
Innovation Solution
A decentralized system that allows vehicles to request data from each other and operations centers, using edge systems and gateway systems to dynamically query and transmit data, enabling iterative data retrieval from multiple sources until all requested data is obtained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized system is used for collecting and analyzing vehicle data, then data management is simplified, but the system becomes inflexible and prone to bottlenecks
Solution Approach 1:
The patent segments the centralized data management system into distributed edge systems deployed across multiple vehicles. Each edge system independently manages and processes data locally, eliminating the single-point bottleneck while maintaining simplified data management through modular, standardized interfaces. This segmentation allows the system to scale horizontally without increasing operational complexity.
Solution Approach 2:
The patent transitions from a single-dimensional centralized architecture to a multi-dimensional distributed network architecture. Data can now flow through multiple paths and dimensions simultaneously - vehicles can query each other directly, query operations centers, or receive data from multiple sources iteratively, adding dimensional flexibility to the system.
2Quantity of substance
If a centralized system is used for data collection, then data storage is consolidated, but bottlenecks occur depending on data volume and analysis requirements
Solution Approach 1:
The patent divides the consolidated data storage function into distributed storage across multiple edge systems. Each vehicle's edge system stores and processes its own data locally, segmenting the data volume burden. This allows parallel processing of data across multiple nodes, dramatically improving overall data processing efficiency while maintaining consolidated data availability through the network.
Solution Approach 2:
The patent implements self-service data processing at the edge, where each vehicle's edge system autonomously queries and retrieves needed data from other vehicles or operations centers without centralized mediation for every operation. This iterative self-service approach eliminates processing bottlenecks by distributing the analytical workload across the entire network.
3Ease of operation
If vehicles use a centralized system for data exchange, then communication protocols are standardized, but real-time data sharing becomes slower
Solution Approach 1:
The patent adds a direct peer-to-peer communication dimension to the standardized protocol framework. Vehicles can exchange data directly through iterative queries with neighboring vehicles, bypassing centralized routing. This multi-dimensional communication approach maintains protocol standardization while achieving faster real-time data sharing through direct network paths.
Data Source
AI summary
Systems and methods are disclosed for exchanging real-time data between vehicle systems by receiving, at a first source system, a request for data from a requestor and, based on determining that the first source system does not have all requested data, querying an operations center for missing data. Then, based on determining that the operations center does not have all missing data, the operations center is queried for a list comprising candidate source systems. Upon identifying a next source system from the list, the next source system is queried for missing data, and the next source system is removed from the list. Based on whether the next source system has all missing data, either all missing data is transmitted to the requestor, or, the process of finding and querying for the missing data is iteratively performed until all missing data is received or the list is empty.