Election-based political optimization for sub-channel assignment and power allocation in non-orthogonal multiple access-based fog computing vehicular network

• Smart mobile and extensive applications in the mobile network are emerging now. Fog computing focuses more on the research to reduce time delay and enhance network quality. • Non-orthogonal multiple access (NOMA) differentiates users having information multiplexed with different power factors and subchannels. This paper presents a new sub-channel assignment and power allocation model for the NOMA-based fog computing vehicular (FCV) network. • Simulated NOMA-based FCV utilizes many communication models to operate. In this case, the problems of subchannel and power assignment are bi-objective optimization ones. Then, a hybrid optimistic approach is developed for resource allocation in NOMA-based FCV using two objective parameters namely energy and subchannel utility. • A dynamic power adjusting factor is described based on the channel conditions of the users. It also presents Election-based Political Optimizer (EBPO) algorithm for NOMA and it is adapted to the user. • The proposed EBPO provided a modest but sufficient energy efficiency of 40.99%, the maximum subchannel utility, and the highest throughput of 674.875 Mbps. The rapid growth of mobile applications and vehicular communication systems requires networks that can deliver both high capacity and minimal delay. A key challenge is the efficient allocation of subchannels and transmission power to balance energy use and communication quality. This study aims to address this issue by integrating Non-Orthogonal Multiple Access (NOMA) with fog computing in vehicular networks. A hybrid optimization framework is proposed, featuring a dynamic power adjustment factor that adapts to varying user channel conditions. To solve the bi-objective problem of energy efficiency and subchannel utility, an Election-Based Political Optimizer (EBPO) is tailored for NOMA-based fog vehicular (FCV) environments. Simulation experiments reveal that the method achieves 40.99% energy efficiency, maximum subchannel utility, and a peak throughput of 674.875 Mbps. In conclusion, the EBPO-driven approach enhances resource allocation, offering a scalable and effective solution for next-generation vehicular fog networks.