Hybrid Fuzzy Neural Network for Joint Task Offloading in the Internet of Vehicles

Abstract

The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi–Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO.