# BSM-Data Reuse Model Based on In-Vehicular Computing
## 2 Related Works  
If connected vehicles are to form the center of future intelligent transportation systems, one of the most glamorous areas that presents many challenges as we progress is transport data sources. There is no doubt that data have great potential and power, and data are the blood that will run through the veins of connected vehicles [8]. Connected vehicles' data can provide the ability to meet the modern interests of drivers like for instance \"What is the best fuel-efficient route\" and go beyond the classical inquiries about shortest-distance or earliest arrival route [9]. In this respect, the increasing focus of the ITS research community towards the topic of data in connected vehicle environment has led to a bustle of works introducing different approaches to the problem of data collection. Indeed, data collection that is based on the recruitment of connected vehicles is recently studied of [10], where an optimal set of vehicles are required to be identified and recruited to carry out an urban data collection in favor of service provider to facilitate users on city-streets. This incentive-based method named REVERS has exploited game-theory to fairly and optimally select the best vehicles under desired coverage, redundancy, and quality requirements. Similarly, to perform urban sensing for a desired coverage, given a limited budget, the authors of [11] proposed the recruitment of high reputation users. Additionally, the authors of [12] have focused on how to maximize message coverage in urban vehicular networks by the optimal deployment of roadside units. In their proposed message coverage maximization algorithm, namely MCMA, the authors have considered traffic stream and delay constraint of applications to attentively identify the appropriate site for RSUs. Some other studies have been conducted on connected vehicles data transmission issues. Accordingly, the authors of [13] have attempted to improve the cooperative data dissemination performance, where the authors considered the cooperative data scheduling decision in hybrid (V2I) and (V2V) vehicle networks as a maximum weighted independent set (MWIS) problem. Subsequently, by solving the MWIS, the goal is to maximize the number of vehicles that retrieve their requested data. In the same way, the work by [14] has dealt with connected vehicles related data distribution tasks, where the authors designed an infrastructure to enable large-scale message delivery by labeling and customizing unstructured data into topics in order to serve a wide range of consumers. Authors of [15] have attempted to cope with the problem of delay-tolerant connected vehicles data traffic delivery. They introduced an architecture where delay-tolerant traffic is offloaded from the data networks to the connected vehicle networks, without extra infrastructure or hardware deployment. The authors have also proposed a distributed data hopping mechanism to allow delay-tolerant data routing over CV networks. In another paper [16], for large-scale vehicular content distribution in urban areas, the authors came up with the idea of deploying a multitude of wireless buffer devices on the roadside, namely roadside buffers (RSBs), to extend the distribution of locally content to vehicles in the urban area. The work by [17] discussed the possibility of substituting RSU by city buses widely distributed in the city area to improve intra-cluster BSM dissemination. The authors have also proposed an allocation mechanism for intra-cluster message distribution. When considering the problem of vehicular datasets, authors of [18] mimicked the ordinary daily road activity of a 400 km\({}^{2}\) region and generated a realistic artificial vehicular mobility dataset. Regarding aforementioned studies, it is important to note that data collection approaches remain ineffective in a dynamic environment, since they are not based on stable criteria for the selection of candidate vehicles that should be important and available to meet the relevant user interests in the network.