Fuzzy Decision Based Energy-Evolutionary System for Sustainable Transport in Ubiquitous Fog Network

These days, the usage of sustainable transport applications has been growing in practice and has minimized global environmental issues as fuel vehicles did. Sustainable transport applications are distributed and can access data from anywhere in the network. However, due to sustainable electrical transport, much digital data is offloaded to the server to obtain the electricity stations. Therefore, many factors challenge sustainable vehicle applications, such as battery power consumption, service searching cost, execution delay, and execution accuracy. Thus, the existing decision support methods, such as TOPSIS multi-criteria decision method (MCDM), only support the fixed and accurate. Therefore, the fuzzy-based strategy will be more optimal for sustainable transport. The study devises the fuzzy-based energy-efficient decision support system (FBEES), which minimizes energy consumption, delay, and cost and increases scheduling accuracy for sustainable applications. These vehicles are connecting to the ubiquitous fog servers at different data centers in the system and offload their data for their processing. Simulation results show that FBEES minimizes energy by 30%, cost by 29%, delay by 31%, and improves accuracy compared to existing methods for sustainable transport applications.