Abstract A Vehicular Ad Hoc Network (VANET) is a specialized form of Mobile Ad Hoc Network (MANET) that incorporates concepts from Intelligent Transport Systems (ITS) and road safety applications. Unlike conventional MANETs, vehicles in a VANET are required to exchange a significant amount of data within a constrained time frame while operating at high speeds, which leads to frequent network instability. Therefore, clustering methods are proposed to stabilize the VANET environment. However, improving the routing efficiency and reducing message overhead remain key challenges to be addressed. To help vehicles receive and transmit road information more quickly and efficiently in high-speed mobile environment, this study proposes a four-step Candidate Cluster Head Nomination Algorithm (CCHNA), which enables vehicles to communicate with others based on specific parameters. The algorithm also includes a CH Pruning Stage, which reduces the number of cluster heads generated during the CH Nomination Stage by comparing these parameters. Experimental results show that the proposed CCHNA reduces the average number of cluster heads by 1–3 compared to other algorithms. During the cluster maintenance stage, message overhead during cluster reorganization is also significantly reduced, achieving up to six times fewer message exchanges. Therefore, CCHNA results in fewer clusters and considerably reduces communication costs associated with cluster information.

This work presents an efficient approach for deriving itemsets with high fuzzy utility values from quantitative data. Each item in a transaction has its own profit and quantity, and the total fuzzy utility of it is considered. We also design a useful strategy to prune unpromising fuzzy candidate itemsets, thus making the mining process efficient. Through a series of experimental evaluations, the results show the proposed approach could perform well in fuzzy utility mining.

In this paper, puncturing and path pruning are combined for convolutional codes to construct a new coding scheme for unequal error protection (UEP), called the hybrid punctured and path-pruned convolutional codes. From an algebraic viewpoint, we show that the hybrid codes not only inherit all the advantages of the conventional rate-compatible punctured convolutional codes and path-compatible pruned convolutional codes but also can provide more flexible choices of protection capability for UEP. In addition, a data-multiplexing scheme originally proposed for path-pruned codes which can guarantee smooth transition between rates without additional zero-padding for frame termination is proven applicable to the hybrid codes to improve the system throughput.