Fog Computing for Vehicle Edge Latency
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Solution Overview
Problem
Centralized backend structures for vehicle computational power create bottlenecks in data transfer and are prone to large-scale disruptions, making it difficult for vehicles to communicate with multiple IoT devices and requiring extensive encryption, while also limiting local computational resources for quick processing.
Innovation Solution
Implementing a fog computing structure with geographically distributed computing devices that process tasks based on proximity criteria, allowing peer-to-peer communication and using blockchain for secure resource management and energy sourcing from local renewable sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized backend structure is used to provide computational power to vehicles, then computational resources can be consolidated and managed centrally, but data transfer bottlenecks occur and communication latency increases
Solution Approach 1:
The centralized backend structure is segmented into multiple distributed computing devices (edge servers, roadside units, cloud data centers) geographically dispersed across different locations. Each device independently provides computational services to vehicles in its coverage area, eliminating the single-point bottleneck and reducing data transfer latency through localized processing.
Solution Approach 2:
The system transitions from a single-dimensional centralized architecture to a multi-dimensional distributed architecture by adding geographical distribution as a new dimension. Computing devices are deployed across multiple spatial locations, allowing vehicles to access computational resources from the nearest available device, thereby reducing communication distance and latency.
2Device complexity
If a centralized backend structure is used, then resource management is simplified, but the system becomes vulnerable to large-scale disruptions and shutdowns
Solution Approach 1:
The monolithic centralized backend is segmented into multiple independent computing devices distributed across different geographical locations. Each device operates autonomously, so a failure or shutdown at one location does not affect the others, ensuring continuous service availability through redundancy and fault isolation.
Solution Approach 2:
The system changes the operational parameter from centralized control to distributed autonomous operation. Each computing device independently manages its local services and communications, allowing the network to maintain functionality even when individual devices or connections fail, thereby improving overall system reliability.
3Productivity
If direct peer-to-peer communication between vehicles and IoT devices is established, then communication efficiency improves, but securing communication requires extensive encryption keys
Solution Approach 1:
Distributed computing devices serve as intermediary nodes between vehicles and IoT devices. These intermediaries establish secure communication channels using standardized protocols, eliminating the need for each vehicle to manage individual encryption keys for every IoT device. The intermediaries handle authentication and encryption centrally, simplifying key management while maintaining security.
Solution Approach 2:
The distributed computing devices provide universal security services to multiple vehicles and IoT devices simultaneously. A single computing device can authenticate and secure communications with numerous devices using a standardized key management system, replacing the need for extensive individual encryption keys while maintaining direct peer-to-peer communication efficiency.
4Speed
If huge computational resources are provided locally in vehicles, then computational speed improves, but it becomes difficult for manufacturers to provide such resources worldwide
Solution Approach 1:
Distributed computing devices act as external computational intermediaries that vehicles can access when additional processing power is needed. Instead of equipping every vehicle with huge computational resources, the system provides on-demand access to powerful edge servers and cloud data centers, achieving high computational speed without increasing manufacturing complexity.
Solution Approach 2:
The system transitions from a static configuration where computational resources are fixed in vehicles to a dynamic architecture where vehicles can flexibly access computational resources from distributed devices based on real-time needs. This dynamic resource allocation allows high computational performance without requiring all vehicles to be manufactured with identical high-end hardware.
Data Source
Figure 1
AI summary
The invention provides a method for delivering network-based computational power (17) to a moving vehicle (10), wherein the computational power (17) is provided by a backend structure (13). The invention comprises that the backend structure (13) is provided as a fog computing structure (16) comprising a plurality of computing devices (15) that are geographically distributed and that are interconnected over the network (14). A computing task (20) is processed by the backend structure (13) on behalf of the vehicle (10), wherein the processing is performed by at least one of the computing devices (15) that fulfills a predefined proximity criterion (22) with regard to the vehicle (10). While the vehicle (10) is moving at least one follow-up computing device (15) is selected that then fulfills the proximity criterion (22) due to the moving of the vehicle (10).