## 5 Conclusions  
In this paper, we propose a computing offloading strategy for IoV that integrates IPLO algorithm and blockchain technology, which realizes efficient allocation and safe control of computing resources in the IoV environment by establishing a single-vehicle multi-tasking computing offloading model and adopting an authorized Byzantine fault-tolerant consensus mechanism. Among them, the IPLO algorithm significantly reduces the system latency, energy consumption, and total overhead by introducing the bounce boundary processing mechanism, while demonstrating excellent stability in high-speed mobile scenarios, while the dBFT consensus mechanism based on the election of equity votes effectively prevents the risk of data tampering by dynamically selecting consensus nodes, providing a reliable foundation for trusting IoV. The scheme provides an efficient, stable, and secure edge computing solution for IoV through the co-design of algorithm optimization and security mechanisms.  
The designed scheme demonstrates great practical value in IoV. In terms of performance improvement, it significantly enhances the real-time and economy of data processing in IoV by reducing system latency, energy consumption, and total overhead, providing a smoother operation environment for IoV applications. At the same time, lower latency can reduce the risk of traffic accidents and improve road safety. In terms of safety and security, the introduction of blockchain technology ensures the integrity of data transmission and effectively guards against the risk of malicious attacks and data leakage. In terms of system stability, the optimized algorithmic design allows the system to maintain reliable operation in scenarios of high-speed vehicle movement and frequent changes in network topology. Together, these advantages provide a solid technical foundation for IoV. In the future, this research will expand to frontier areas, such as quantum computing optimization and intelligent consensus mechanism, and is committed to breaking through the existing technical bottlenecks and helping the IoV technology to move to a higher level of development.  
Conceptualization, Y.L. and Y.D.; methodology, Y.L. and B.Y.; software, Y.L. and B.Y.; validation, B.W. and Q.S.; investigation, B.W. and Q.S.; writing--original draft preparation, B.Y. and B.W.; writing--review and editing, Y.L. and B.Y.; supervision, Y.L. and Y.D. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Collision** & **Fixed Data Volume** & **Fixed CPU Cycles and** & **Fixed CPU Cycles** \\
**Probability** & **and Number of Tasks** & **Number of Tasks** & **and Data Volume** \\ \hline
0.01 & 0.450215907 & 0.40370882 & 0.615417646 \\
0.03 & 0.451200618 & 0.397350783 & 0.619286688 \\
0.05 & 0.451540167 & 0.400150143 & 0.616115548 \\
0.07 & 0.450900238 & 0.399467543 & 0.618433646 \\
0.1 & 0.45002346 & 0.403075415 & 0.616594892 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average overhead of IPLO algorithm under different collision probabilities.  
**Funding:** This research was supported by a fund from the Natural Science Foundation Program of Jilin Province: 20250102232 JC; Key Scientific Research Project of the Jilin Provincial Department of Education: JKH20251099 KJ.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.