## 3 Optimization Problems in Vehicular Networks
### Optimization Challenges in Traffic Improvement Application of Vehicular Networks
Considering message transmission results in unwanted data flooding, which causes broadcast storm issues, affecting the overall reliability and performance of VANET. To efficiently minimize the broadcast storming problem, [PERSON] and [PERSON] [88] proposed a relative speed-based dynamic broadcasting GHN algorithm for broadcasting safety and warning messages in the VANET. For data transmission, the GHN algorithm employs the selective distance allocation methodology. They compared it with SODAD and ABIN, and the results demonstrated that the GHN algorithm can reduce the broadcast storm by more than 2% when compared to the existing algorithm. The system's output proved to be more efficient in terms of data, throughput, and packet delivery ratio. Table 1 summarizes the various approaches of the reviewed literature, as well as the objective and specific methods used for various types of problems. Each issue made use of various vehicle communications and protocols, which have also been mentioned. \begin{table}
\begin{tabular}{|p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}|} \hline
**Article** & **Year** & **Objective** & **Methods** & **Type** & **Vehicle Communication** & **Case Study** \\ \hline
[6] & 2019 & Obtain optimal offloading policy & A3C algorithm, DBI & offloading decision VEC & WLAN & - \\ \hline
[62] & 2019 & Minimize storage cost on large time scale & Joint optimization problems & Content caching VEC & V2V, RSU & - \\ \hline
[63] & 2020 & Minimize task processing time & Mark Main Fairness scheme, Partic Swarm Optimization (PSO) & Computation offloading VEC & V2V, 802.11p, W985 & - \\ \hline
[64] & 2020 & Minimizing system energy consumption satisfying task latency constraints & SSV clustering, imitation learning approaches, branch-and-bound algorithm & Online task scheduling & BSU & Hangzhou, China \\ \hline
[64] & 2013 & Schedule requests in real time, minimize the servers’ travelling times & Kinetic tree algorithm, the skact time algorithm, hot-spot scheduling algorithm & Dynamic redisbarring schedule problem & - & Shanghai, China \\ \hline
[67] & 2014 & Minimizing passenger waiting time & Security Matching Method, fuzzy clustering & Distributed ridesharing matching problem & V2V, 802.11p, GPS & - \\ \hline
[68] & 2018 & Minimize total distance traveled maximize occupancy & Picking and dope of in different state. Last Distance Minimization Algorithm, DDISCAN Clustering & Distributed DITS scheduling problem & GPS & Shanghai, China \\ \hline
[69] & 2022 & Minimize waiting time and travel distance clustering & E exact method based on branch-and-cut Relling horizon for dynamic situation known, hierarchical clustering & Real-time redisbarring matching problem & GPS & Lyon city, France \\ \hline
[70] & 2020 & Optimizing matches by considering characteristics Maximizing a joint socialiness score & Needleman-Wunsch algorithm, Matching process with first-cone-first-serve & Ridesharing matching problem & - & Istanbul city, Turkey \\ \hline
[71] & 2016 & Serve the users’ mobility needs without their active participation & Application layer, distribution layer, and an ad-hoc communications layer & Proactive ridesharing & VNADOV\({}_{*}\)=802.11p & - \\ \hline
[72] & 2021 & Much a minimum number of rides with drivers & Trade and similarity models, 1-to-R discharge shortest routes and Pickup points recommendation & Recommending the shortest routes and pickup points for ridesharing & VNAeLayer & - \\ \hline
[74] & 2014 & Minimizing waiting time & VSCA, DSGA, Fuzzy fusion, fuzzy clustering & Cashsharing matching problem & SGA, 802.11p & - \\ \hline \end{tabular}
\end{table} 
Table 1: Summarized reviewed work.