# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles  
[PERSON]  
1 Department of Computer Science and Engineering, Kyung Hee University, Gyeonggi-do 17104, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 Humanitas College, Kyung Hee University, Gyeonggi-do 17104, Korea 22 Humanitas College, Kyung Hee University, Gyeonggi-do 17104, Korea 2  
[PERSON]  
3 Department of Software Convergence, Kyung Hee University, Gyeonggi-do 17104, Korea; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-31-201-371135 February 201919 February 20191919 February 20191919 February 20191919 February 20191919 February 20191919 February 20191919191919191919191919191919191  
V2X communications is 1200 bytes. Assuming that 70 million nodes transmit data with these payloads once every 10 seconds, the maximum payload reaches 1,933 PB per month (peta bytes per month). In the IoT era, the total amount of network nodes as well as vehicle nodes is expected to increase significantly. According to reference [4], the IoT installed base is estimated to reach 30.73 billion by 2020. Rather than centrally control this vast amount of traffic through a cellular network, efforts have been made to reduce the load on the control by constructing a mesh network and a VANET between the road side unit (RSU) and the on-board unit (OBU). For example, there is geocast routing using zone of relevance (ZOR), directional data dissemination protocol using angles between nodes, and the distributed and mobility-adaptive clustering algorithm (DMAC) considering the speed difference of nodes [5; 6; 7].  
Therefore, this paper proposes a clustering algorithm using path information and a data dissemination algorithm between clusters. The main content of this paper is as follows.  
First, we propose a clustering algorithm using path information by improving the related research. In a related study, it was assumed that each vehicle node is G/G/1 Queue and the vehicle follows Brownian motion. In this case, the authors modeled the node's successful transmission rate as the probability that nakagami-m fading would not occur at all or would occur within a certain distance [8; 9]. However, this model ignores the fact that vehicle traffic can be modeled as a Markov chain Monte Carlo system (MCMC) because the actual road environment does not know the traveling paths of other vehicles [10]. Therefore, the vehicle position cannot be considered to follow the complete Wiener motion, and it is correct to model each intersection with MCMC using numerous iterations.  
However, if the path prediction is more accurate than the MCMC in the autonomous mobile era, the probability that nodes in the same cluster are in the same segment for a longer period of time converges close to 100%. As a result, you can dramatically improve the lifetime of your cluster. Additionally, as the lifetime of the cluster increases, the overhead of the cluster reconstructing process decreases and the ratio of lost data dissemination messages during the process can be reduced.  
Second, we developed a data propagation algorithm that can increase the information propagation rate of the entire network by using path information. The cluster head is propagated using the member closest to the neighboring cluster as a gateway role, and the neighboring cluster is a cluster directed to another path. The cluster temporarily stores the data to be deployed, and then distributes it when it encounters another neighbor cluster. Since the cluster that receives the data is a cluster with a different path, the cluster that is newly encountered is likely to be a cluster that is encountered in another path. Thus, the data can spread even in non-contiguous clusters.  
In summary, this paper proposes clustering-based data dissemination algorithms and protocols that use vehicle paths for autonomous navigation and compares them with existing clustering-based data dissemination algorithms using possibilities.