# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 2 Related Work  
The conventional RSU deployment schemes mainly focus on network connectivity, traffic coverage, and information dissemination. Early in 2008, [PERSON] et al. proposed a genetic algorithm that was able to find good positions for static RSUs in order to cope with the highly partitioned nature of a VANET in an early deployment stage [3]. In 2010, [PERSON] et al. considered a given number of infrastructure nodes (called Dissemination Points, DPs) to be deployed for disseminating information to vehicles in an urban area, formulating the problem as a Maximum Coverage Problem (MCP) [4]. In 2013, [PERSON] et al. proposed a density-based roadside unit deployment policy (D-RSU) specifically designed to create an efficient system with the lowest possible cost to alert emergency services in the case of an accident. This was based on deploying RSUs in inverse proportion to the expected density of vehicles [5]. [PERSON] et al. [6] proposed an RSU deployment scheme that focuses on the one-dimensional RSU deployment problem. The results of their greedy algorithm typically yield solutions close to the optimal. In view of the deployment cost of RSUs and the low market penetration of VANETs, [PERSON] et al. aimed to deploy a minimum number of RSUs [7]. [PERSON] et al. [8] introduced a heuristic algorithm based on Greedy Randomized Adaptive Search Procedure 2-multi-hop (GRASP2 MH) to deploy a minimum number of RSUs to meet quality of service (QoS) requirements. [PERSON] et al. addressed the problem of the RSU deployment scheme with constraints on delay and cost [9]. They first transformed the road network diagram into a weighted graph and then formulated the problem as a variant of the 0-1 knapsack problem. [PERSON] et al. introduced a new RSU deployment strategy that is more efficient by increasing V2I access opportunities compared with the simple use of the 0-1 knapsack algorithm [10]. [PERSON] et al. proposed an approach that enhances vehicles' connectivity coverage threshold time in VANETs across different traffic scenarios by integrating betweenness centrality preprocessing, community detection, and efficient search space reduction techniques [11]. [PERSON] et al. [12] highlighted that even with sparsely distributed roadside units, effective traffic predictions can be achieved by placing RSUs strategically, considering communication ranges and penetration rates of connected vehicles, and optimizing the  
Figure 1: V2X framework with parked cars serving as t-RSUs in a VANET environment. deployment strategy. All those RSU deployment schemes mentioned above are constrained with the precondition that the parked cars are not utilized to act as temporary roadside units. Moreover, [PERSON] et al. propose two new deployment strategies for RSUs in intelligent transportation systems: Gamma Deployment and Gamma-Reload Deployment, aiming to optimize the deployment of RSUs for delivering streaming content to connected vehicles, ensuring that a specific share of vehicles periodically encounter the communication infrastructure within a given time threshold [13]. [PERSON] et al. have developed a comprehensive mathematical model that captures the essential elements of RSU deployment, including network, communication, and computation. Their proposed solution aligns with the specific communication requirements of road vehicles [14]. In fact, as early as 2011, [PERSON] et al. first proposed the idea that the parked cars join VANETs as static nodes, assisting to improve connectivity [15]. Later, in 2014, [PERSON] et al. addressed the challenges of radio signal attenuation caused by buildings and other obstacles, proposing that parked cars are a means to mitigate these issues [16]. In 2017, [PERSON] et al. introduced a self-organizing network approach allowing parked cars to function as RSUs, forming a vehicular support network [17].