LAUSR

With the significant research and advancements in technologies, arose new applications such as autonomous driving and augmented/virtual reality. These applications required massive computational resources for the execution of various tasks. Utilizing vehicles resources in a distributed manner and collectively with the help of volunteer computing for various computational tasks is an emerging research area. The appropriate and intelligent decision in selecting a volunteer vehicle is crucial in this opportunistic network where information is exchanged between vehicles. In this paper, we propose Intelligent Volunteer Computing-based VANETs architecture to fulfill the computational requirements of vehicles applications intelligently. We propose selection criteria to select volunteers’ vehicles capable of the execution of the computationally intensive task. In this study to rightly identify the volunteer vehicle for task execution, we use a machine learning approach that predicts the capability of certain vehicles in completing the task. Extensive experimentation is conducted for the prediction of the computing capability of optimal volunteer vehicles. We used nine different regression techniques on publicly available datasets. The results show these techniques can efficiently predict the capability of volunteers. By comparing the regression techniques, the results indicate that the ridge regression and support vector regression can significantly reduce the mean square error, relative absolute error, and root mean square errors. Simulations are conducted to compare the proposed scheme with the existing one.