# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 1 Introduction  
Vehicle-to-Vehicle (V2V) communication stands out as a pivotal technology in the field of autonomous driving [1]. The exchanged content can be categorized into two types: content related to safety and content related to comfort. The former typically has a brief lifespan and is seldom requested again. In comparison, the latter has a longer lifespan, and various vehicles may request the same information multiple times. When this type of content is delivered through separate pathways, the traffic volume increases rapidly with the growing number of requesting vehicles. The Content-Centric Network (CCN) architecture [2] is specifically designed for the repeated delivery of identical content. In CCN, nodes use interest packets to request content and data packets to deliver it. Each node stores received or forwarded content as caches, enabling them to respond to requests instead of content providers. This approach effectively reduces communication delays and network traffic, making CCN widely adopted in wired networks. Recently, there has been a growing interest in applying CCN to vehicular networks [3, 4, 5], where a vehicle is treated as a CCN node. There are limitations and issues with the basic CCN, however. 1. This approach cannot use content cached at nearby nodes that are not on communication routes. 2. Nodes autonomously decide whether to cache content, leading to the possibility of multiple vehicles storing the same information. This consumes cache buffers uselessly and hampers the caching of other content. 3. The mobility of vehicles makes it complex to establish concrete communication paths. Collaborative caching provides an effective solution to address (1) and (2). As for (1), Ref. [6] solves it by letting vehicles periodically broadcast beacons informing the names of cached content with great overhead due to the extensive communication cost. As for (2), Refs. [3; 7; 8] suggest that each vehicle make a probabilistic decision on whether to cache content. While this reduces the redundancy of storing identical content in nearby vehicles, it does not completely eliminate it. To further improve the performance of vehicular networks by CCN, this paper proposes a novel collaborative caching method. This method groups vehicles into clusters and manages caches in each cluster unitarily. Specifically, each cluster comprises a cluster head (CH) and several cluster members (CM). A CH is responsible for dealing with content requests and managing content in its cluster. When a CH receives a content request, it searches for the requested content in its cluster, enabling the CH to respond to requests based on caches from any vehicles within its cluster. When a CH receives content, it determines whether to store it in its cluster and if it does, it also decides which vehicle in its cluster stores it, which prevents duplicated content from being stored in a cluster. The contributions of this paper are two-fold as follows:  
* To the best of our knowledge, this is the first work that proposes to consolidate content management within a cluster to optimize the utilization of cached content and cache buffers in V2V communications. * We implement unitary cache management by letting each vehicle overhear data packets, which suppresses the control overhead. Part of this paper has been presented in a conference [9]. In this work, we added the estimation of content popularity and on this basis refined cache management. In addition, we evaluated the proposed method in more scenarios: both the freeway scenario used in [9] and an urban model scenario that has been newly added in this paper. We also added a comparison with ECV+ [3]. Extensive simulation evaluations demonstrated that the proposed method effectively increased the cache hit rate within a cluster and improved network efficiency. The rest of this paper is organized as follows. Section 2 reviews the basic CCN and related works. Section 3 presents the proposed method, and Section 4 confirms its effectiveness by simulation evaluation under different settings.