Big data traffic management in vehicular ad-hoc network

Abstract

<span>Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.</span>

Big data classification based on improved parallel k-nearest neighbor

“…The twenty-first century is the era [2] in which there is an enormous quantity of data, and that number is growing at an alarming rate. When dealing with such large amounts of data, the usual performance of computers is insufficient, and more intelligence is required to keep up with the current expansion of big data [3]- [17]. High-performance computing (HPC) should be taken into account while designing and creating software since it has the potential to increase computation speed while also giving more accurate results at a lower total cost than traditional computing [18]- [25].…”

Big data vehicle density management in vehicular ad-hoc network

“…1, January 2024: 314-323 316 architecture and hadoop eco system. Mouad et al [24], proposed a big data-based technique to balance different roads of the city by directing vehicles to their destination relying on the estimated time of arrival. The research paper identified as [25], introduces a method that proposes a flexible transfer especially for VANET by including fog computing and software defined networking (SDN) technologies.…”

“…For the problem of trajectory planning for fully autonomous vehicles, this work presents a method of optimal and safe trajectory selection in autonomous vehicles, the weak point of this method is in the criterion of trajectory selection, the estimated time of arrival is calculated for each segment by dividing the length of each segment by the speed limit assigned to this segment, it does not take into account the variations in the state of the traffic at time t. Our method takes into account frequent changes in traffic conditions in real-time, it makes it possible to control the traffic, detect risky situations on the different roads and then direct vehicles to optimal paths in terms of speed and safety, and it can also predict accidents and warn vehicles of the risk. In our previous method [24], its priority was speed more than safety; its main metric was the travel time on different sections of road. On the other hand, in this paper, we built a method whose main metric is the density of the vehicle in the different road sections, which will result in a better balance of the load, and therefore this last technique is better in terms of road safety.…”