A Novel Energy-Minimization Joint Hungarian-PSO Algorithm for Task Offloading in Vehicular Fog Computing

Wu Wang

School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China

*Corresponding author

With the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, Vehicular Fog Computing (VFC) has emerged as a crucial technology in Intelligent Transportation Systems (ITS). Fog computing is a distributed computing model. VFC is a network architecture formed by applying fog computing technology to the Internet of Vehicles (IoV). In VFC, vehicles can offload tasks to other vehicles or roadside units with more powerful resources, leveraging their idle computing power to boost system performance. As vehicles usually run on batteries, reducing energy consumption can extend battery life, support more computing tasks, and cut down device maintenance and replacement frequency. Therefore, how to efficiently perform task offloading to minimize energy consumption has become a key research challenge. This paper proposes a joint task offloading scheme based on the Hungarian algorithm and Particle Swarm Optimization (PSO) algorithm to optimize energy consumption and latency in fog computing networks. First, the Hungarian algorithm is employed to achieve optimal matching between User Vehicles (UVs) and Fog Vehicles (FVs), ensuring efficient task allocation. Subsequently, the PSO algorithm is utilized to optimize transmission power, further reducing energy consumption and interference. Experimental results demonstrate that the proposed scheme significantly reduces the system’s total energy consumption while meeting task latency constraints. Simulation results also show that the scheme exhibits excellent performance and stability in complex network environments, providing an effective solution for task offloading in fog computing networks.

Vehicular fog computing, Energy-Minimization, Task offloading, Hungarian algorithm, Particle Swarm Optimization algorithm

Lin Chai, Jun Wang, Shuhui Fang, Yumei Yang, and Wu Wang (2025). A Novel Energy-Minimization Joint Hungarian-PSO Algorithm for Task Offloading in Vehicular Fog Computing. Journal of Networking and Network Applications, Volume 5, Issue 2, pp. 55–63. https://doi.org/10.33969/J-NaNA.2025.050201.

[1] X. Wu, S. Zhao, and H. Deng, “Joint task assignment and resource allocation in vfc based on mobility prediction information,” Computer Communications, vol. 205, pp. 24–34, 2023.

[2] C. Liang, Y. Zhao, Z. Gao, K. Cheng, B. Wang, and L. Huang, “Ealso: joint energy-aware and latency-sensitive task offloading for artificial intelligence of things in vehicular fog computing,” Wireless Networks, vol. 31, no. 1, pp. 583–599, 2025.

[3] G. Zhang, F. Shen, Z. Liu, Y. Yang, K. Wang, and M.-T. Zhou, “Femto: Fair and energy-minimized task offloading for fog-enabled iot networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4388–4400, 2018.

[4] J. Kim, T. Ha, W. Yoo, and J.-M. Chung, “Task popularity-based energy minimized computation offloading for fog computing wireless networks,” IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1200–1203, 2019.

[5] Z. Lin, X. Chen, X. He, D. Tian, Q. Zhang, and P. Chen, “Energy-efficient cooperative task offloading in noma-enabled vehicular fog computing,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7223–7236, 2024.

[6] A. Yadav, P. K. Jana, S. Tiwari, and A. Gaur, “Clustering-based energy efficient task offloading for sustainable fog computing,” IEEE Transactions on Sustainable Computing, vol. 8, no. 1, pp. 56–67, 2022.

[7] M. Hussain, M. Saad Alam, M. Sufyan Beg, and N. Akhtar, “Towards minimizing delay and energy consumption in vehicular fog computing (vfc),” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6549–6560, 2020.

[8] M. M. Hussain, A. T. Azar, R. Ahmed, S. Umar Amin, B. Qureshi, V. Dinesh Reddy, I. Alam, and Z. I. Khan, “Song: A multi-objective evolutionary algorithm for delay and energy aware facility location in vehicular fog networks,” Sensors, vol. 23, no. 2, p. 667, 2023.

[9] X. Hou, Y. Li, M. Chen, D. Wu, D. Jin, and S. Chen, “Vehicular fog computing: A viewpoint of vehicles as the infrastructures,” IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860–3873, 2016.

[10] R. Yadav, W. Zhang, O. Kaiwartya, H. Song, and S. Yu, “Energy-latency tradeoff for dynamic computation offloading in vehicular fog computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14 198–14 211, 2020.

[11] A. Pratap, R. Misra, and S. K. Das, “Maximizing fairness for resource allocation in heterogeneous 5g networks,” IEEE transactions on mobile computing, vol. 20, no. 2, pp. 603–619, 2019.

[12] X. Huang, L. He, X. Chen, L. Wang, and F. Li, “Revenue and energy efficiency-driven delay-constrained computing task offloading and resource allocation in a vehicular edge computing network: A deep reinforcement learning approach,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8852–8868, 2021.

[13] C. Liang, Z. Gao, B. Wang, K. Cheng, and Y. Zhao, “Qeclo: A novel qos-aware joint optimization of energy and latency for vfc task offload-ing,” in 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2024, pp. 1814–1819.

[14] C. E. Shannon, “A mathematical theory of communication,” The Bell system technical journal, vol. 27, no. 3, pp. 379–423, 1948.

[15] S. Xia, Z. Yao, Y. Li, and S. Mao, “Online distributed offloading and computing resource management with energy harvesting for heteroge-neous mec-enabled iot,” IEEE Transactions on Wireless Communica-tions, vol. 20, no. 10, pp. 6743–6757, 2021.

[16] S. Zhang, Y. Xue, H. Zhang, X. Zhou, K. Li, and R. Liu, “Improved hungarian algorithm–based task scheduling optimization strategy for remote sensing big data processing,” Geo-spatial information science, vol. 27, no. 4, pp. 1141–1154, 2024.

[17] A. G. Gad, “Particle swarm optimization algorithm and its applications: a systematic review,” Archives of computational methods in engineering, vol. 29, no. 5, pp. 2531–2561, 2022.

[18] M. Jain, V. Saihjpal, N. Singh, and S. B. Singh, “An overview of variants and advancements of pso algorithm,” Applied Sciences, vol. 12, no. 17,

p. 8392, 2022.