Autonomous Vehicles as Mobile Edge Data Nodes in Smart Cities
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Solution Overview
Problem
There is currently no effective means for enabling autonomous vehicles to function as mobile edge data centers in smart city infrastructure, as they may be parked in areas with low data demand and it is impractical to create parking zones based on dynamic data demand areas.
Innovation Solution
A computer-implemented method that utilizes autonomous vehicles as mobile edge data nodes by predicting data demand in a smart city region, identifying available parking spots and autonomous vehicles, and instructing them to park in strategic locations to assist in servicing the predicted data demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles are parked in fixed locations, then data storage and distribution services can be provided, but the vehicles may be located in areas with low data demand, reducing service efficiency
Solution Approach 1:
The patent makes the autonomous vehicle fleet dynamic by enabling real-time repositioning based on predicted data demand. The system continuously monitors data demand predictions and automatically redistributes vehicles from low-demand to high-demand regions, transforming the static parking model into a dynamic resource allocation system that adapts to changing conditions.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where data demand predictions are continuously fed back to the vehicle management system. This feedback drives automated repositioning decisions, creating a responsive system that adjusts vehicle locations based on actual and predicted demand patterns, thereby optimizing service efficiency while maintaining reliability.
2Productivity
If parking zones are created based on dynamic data demand, then data service efficiency improves, but the complexity of managing and updating parking zones increases
Solution Approach 1:
The patent applies multi-functionality by using autonomous vehicles for multiple purposes: they serve as both transportation units and mobile data centers. This universal approach eliminates the need for dedicated parking zones, as vehicles can dynamically serve any location requiring data services, thereby reducing management complexity while maintaining high service efficiency.
Solution Approach 2:
The system enables self-service through automated vehicle repositioning based on demand predictions. The intelligent transport system automatically identifies vehicles needing repositioning and directs them to appropriate locations without manual intervention, reducing the operational complexity of managing dynamic parking zones while optimizing data service delivery.
3Quantity of substance
If more autonomous vehicles are deployed to high data demand areas, then data caching capacity increases, but the cost of vehicle deployment and management increases
Solution Approach 1:
The patent merges multiple vehicle functions by combining data storage, processing, and distribution capabilities within the existing autonomous vehicle infrastructure. This integration allows the system to increase data caching capacity by utilizing already-deployed vehicles rather than acquiring additional dedicated data center vehicles, thereby avoiding the high costs associated with separate vehicle deployment and management.
Data Source
AI summary
A computer-implemented method, system, and computer program product utilizing autonomous vehicles as mobile edge data nodes for data storage and distribution. Information pertaining to data being generated and used in a smart city is collected to predict the data demand in a region of the smart city. If the predicted data demand for a region of the smart city exceeds a threshold value (capacity to service the predicted data demand), then one or more available parking spots in this region of the smart city are identified. Furthermore, one or more available autonomous vehicles functioning as mobile edge data nodes to assist in servicing the predicted data demand in this region of the smart city are identified. Such identified autonomous vehicles may then be instructed to park in one of the identified available parking spots to assist in servicing the predicted data demand in this region of the smart city.