Vehicle Clustering via Link Reliability and Stability
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Solution Overview
Problem
The frequent exchange of small data packets between vehicles and base stations in the Internet of Vehicles leads to network congestion, affecting connectivity and real-time performance, particularly due to the reliance on dedicated short-range communication (DSRC).
Innovation Solution
A vehicle clustering method based on link reliability and stability, which involves acquiring vehicle dynamic information, clustering vehicles, calculating link reliability and stability, determining a selection priority index, and selecting a cluster head to optimize communication within the cluster, using device-to-device (D2D) communication to reduce network load and ensure reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles periodically report traveling information to base stations using DSRC, then information exchange is achieved, but network congestion occurs and real-time performance deteriorates
Solution Approach 1:
The patent segments vehicles into clusters with designated cluster heads, dividing the communication task from individual vehicle-base station interactions to cluster-head-mediated communications. This segmentation reduces the number of direct connections to base stations, alleviating network congestion while maintaining connectivity reliability.
Solution Approach 2:
Cluster heads act as intermediaries between member vehicles and base stations. Instead of every vehicle directly communicating with base stations, vehicles communicate through their cluster head, which aggregates and forwards information. This intermediary role reduces network load and improves real-time performance while maintaining reliable connectivity.
2Ease of operation
If cluster head selection is based on traditional methods, then cluster formation is achieved, but link reliability and stability are insufficient
Solution Approach 1:
The patent changes the selection parameters for cluster heads from traditional single-criterion methods to a composite evaluation system considering multiple parameters including link reliability, link stability, vehicle speed, and position. This multi-parameter approach ensures selected cluster heads have optimal communication capabilities, improving link reliability while maintaining ease of cluster formation through automated scoring.
Data Source
AI summary
A vehicle clustering method includes: acquiring vehicle dynamic information, the vehicle dynamic information including vehicle positions and vehicle speeds; clustering vehicles based on the vehicle dynamic information; calculating, based on the vehicle dynamic information, a link reliability and a link stability of the vehicles in a cluster; calculating, based on the link reliability and the link stability, a selection priority index for a cluster head; and selecting a vehicle with a largest selection priority index in the cluster as a cluster head.