## 5 Results and Discussion
### Implication of the Study to Theory and Practice  
It was found in the study that it is possible to develop a vehicle collision alert system for detecting collisions of vehicles ahead of time in the IoV environment using three subset features with a high level of accuracy. The IoV vehicle collision detection features were reduced to a minimum and the accuracy of detecting collision in the IoV was maximized. This indicates that the quality of the data and performance of the algorithm were improved  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Accuracy** & **Time (s)** \\ \hline Pareto envelope-based selection algorithm II & 77.65 & 3.2010 \\ Multi-objective evolutionary algorithm based & 89.65 & 1.2350 \\ on decomposition & 82.62 & 1.8650 \\ Niched Pareto genetic algorithm & 78.66 & 2.7850 \\ Multi-objective genetic algorithm & 89.11 & 1.9870 \\
**Hybrid NSGA3 + GR + Bi-directional** & **99.97** & **0.6963** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the proposed hybrid NSGA3 with other classes of algorithms.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{2}{c}{**Feature Selection Method**} & \multicolumn{3}{c}{**Computational Time (s)**} & \multicolumn{1}{c}{\multirow{2}{*}{**Average Time**}} \\  & & **SVM** & **KNN** & **GNB** & **RFC** & **DTC** & **Average Time** \\ \hline \multirow{2}{*}{Filter} & NSGA2 + GR & 0.742 & 0.891 & 0.811 & 0.742 & 0.749 & 0.787 \\  & NSGA3 + GR & **0.681** & **0.723** & **0.743** & **0.681** & **0.749** & **0.715** \\ \multirow{2}{*}{Wrapper} & NSGA2 + Bi-directional & 0.779 & 0.936 & 0.852 & 0.779 & 0.786 & 0.826 \\  & NSGA3 + Bi-directional & 0.712 & 0.756 & 0.776 & 0.712 & 0.783 & 0.748 \\ \multirow{2}{*}{Hybrid} & NSGA2 + Bi-directional + GR & 0.712 & 0.807 & 0.777 & 0.712 & 0.749 & 0.751 \\  & **NSGA3 + Bi-directional + GR** & **0.696** & **0.739** & **0.76** & **0.696** & **0.766** & **0.731** \\ \multirow{2}{*}{Average time} & & 0.72 & 0.809 & 0.786 & 0.72 & 0.764 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational time for the feature selection algorithms.  
as a result of feature selection. When developing vehicle collision alarm systems to be deployed in the edge of the IoV to provide service to vehicles moving on roads in the IoV environment, a small vehicle collision alarm device requiring a small dataset for its smooth operation can adopt the three subset features without the need to acquire a large-scale dataset. This can avoid the utilization of costly computing resources in subsequent data collection. This is highly relevant, especially in the situation where computing resources and data are highly restricted. This study can help researchers in the future to avoid using large-scale computing resources in acquiring data to develop collision alert system in the IoV in view of the fact that only the subset features discovered in this study will be collected, as opposed to wasting time collecting all features that can come with irrelevant, redundant and noisy features. The complex nature of the alarm systems for detecting IoV vehicle collision can be reduced because the size of the data is at the minimal level, thereby requiring minimal memory space in the computing device. Thus, this can reduce the hardware size required for developing collision alert systems, thereby making the vehicle collision alert system devices cheaper compared to similar alert devices that require large-size hardware. This is so because the larger the physical size of hardware, the higher the cost of the device, as the size of the hardware device reduces the cost (the size of the hardware device is directly proportional to the cost).