# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 4 Scenario Description and Basic Definition
### The Concept of the t-RSU and Its Mechanism
To solve the problem, we can use the online algorithm to make every single tiny area constrained by a parked car's 2-hop transmission range be in a locally optimum state. In Figure 4, we give the detailed process of the online algorithm which enables a practical implementation for the limited computation resources circumstance. After implementing the online algorithm for every single parking lot simultaneously in a distributed manner, it is crucial to set the strict candidate t-RSU area selection strategy that ensures the RSU functions effectively as a real RSU in the real world. The candidate t-RSU area selection strategy is simple but strict: First, the entire parking area is at least parked with one car for over 95% of the duration a day, a condition that can be investigated through a detailed transportation survey. Second, the parking area is located along the roadside on the ground. Figure 3: The formation process of t-RSU and its parameter description. The algorithm to calculate the exact score for determining whether the specific parked car should serve as t-RSU is shown in Algorithm 1. ```
Input:\(SCA=\{sca_{1},sca_{2}\ldots sca_{n}\},ECA=\{eca_{1},eca_{2}\ldotseca_{n}\},\ S(RCA)\) Output: the outcome score of weighted S(SCA, ECA), S(RCA)  Set \(S(SCA,\ ECA),\ S(RCA)\) as 0 for each \(sca_{i}\) in \(\{sca_{1},sca_{2}\ldots sca_{n}\}\)do if\(ca_{i}<sca_{i}\) and \(sca_{i}\) has overlap with \(eca_{i}\)then \(S(SCA,ECA)=S(SCA,ECA)+(sca_{i}-overlap(sca_{i},eca_{i}))\) End \(S(RCA)=S(RCA)+overlap(sca_{i},eca_{i})\) End \(Score=w_{1}S(SCA,ECA)-w_{2}S(RCA)\)
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**Algorithm 1**t-RSU Score  
Detailed explanations of the parameters shown in Algorithm 1 are given below:  
* \(SCA\) represents the self-coverage area of the parked car, and \(sca_{1},sca_{2}\ldots sca_{n}\) are the \(n\) partitions of the self-coverage area. * \(ECA\) is the existing coverage area covered by other temporary t-RSUs; \(eca_{1},eca_{2}\ldotseca_{n}\) are the \(n\) partitions of the temporary coverage area. * \(RCA\) is the redundant area that overlaps between the currently parked car and other temporary t-RSUs. Meanwhile, since the overlapped area is 3D in the real world (due to the existence of underground and above-ground garages), here we use \(overlap(sca_{i},eca_{i})\) for calculating it by projecting the area in the 2D map. * \(w_{1}\) and \(w_{2}\) are the coefficients for the \(S(SCA,\ ECA)\) and \(S(RCA)\), respectively, which can be used to determine the score and set the threshold for whether a new parked car should become a t-RSU. Algorithm 1 presents the pseudo-code to calculate the score for determining whether a single parked vehicle can function as a t-RSU. It quantitatively calculates the influence of each variable and outputs a score. If the score is positive, the parked vehicle is designated as a t-RSU. Otherwise, it is not. Figure 4: Online algorithm. In fact, concerning the real world, from an intuitive perspective, the distribution of parking lots is highly relevant to the prosperity of the district in a city. The central districts' parking lots density is higher than the remote districts since there are more cars in the central rather than the remote. Though it is hard to compute the optimized strategy result of the entire city with all the data centralized at the same time, we can certainly solve this problem in a distributed manner. Furthermore, all the algorithms proposed above are just for calculating whether a specific area is with \"a constant t-RSU\" quantitively. For the future RSU installation deployment, we take multiple parked cars in the parking area and classify them into different clusters. Since we have already taken the parked cars' two-hop maximum transmission range for a single partition in a given area, this strategy automatically forms a mesh network cluster.