FGCF: fault-aware green computing framework in software-defined social internet of vehicle

The social internet of vehicle (SIoV) is a specialized network combining intelligent sensing devices and vehicular communications to address traffic monitoring and resource management challenges in smart cities. Ensuring efficient and sustainable green computing with global network stability is crucial, especially in the dynamic environment of vehicular mobility. The software-defined-SIoV (SD-SIoV) architecture separates control and forwarding planes for centralized management. The architecture addresses green traffic data dissemination with heterogeneous traffic data by formulating control plane nodes’ election as an NP-Hard optimization problem, considering parameters, e.g., transmission distance, node’s residual energy, load imbalance, and mobility factor. The architecture incorporates the random way-point mobility (RWPM) model for simulating nodes’ mobility. The proposed improved energy-efficient gray wolf optimization (IEEGWO) algorithm enhances energy-efficiency by intelligently electing and re-electing optimal control plane nodes, jointly addressing load imbalance and fault-tolerance issues, ultimately improving green computing and communication performance in SD-SIoV. Comparative analysis with state-of-the-art demonstrates that IEEGWO provides significant green computing benefits in a real-time SIoV scenario