Enhancing clustering stability in VANET: A spectral clustering based approach
Gang Liu, Nan Qi, Jiaxin Chen, Chao Dong, Zanqi Huang
Abstract
Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network (VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility (SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.