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Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance. [ABSTRACT FROM AUTHOR]

Intermittency in wind offers the major challenge in accomplishing the wind energy as a dependable sustainable energy resource in power grid. Fluctuations in wind speed occur seasonally over a year and if this seasonality is considered, the prediction of the speed of wind can be made more accurate. In this paper, an attempt is made to apply a signal decomposition technique called Variational Mode Decomposition (VMD), which decomposes series of wind speed data into several intrinsic mode functions (IMFs) to make the data more regular thereby enhancing the accuracy of the wind speed forecast model. Then, artificial intelligence technique, Adaptive neuro fuzzy inference system (ANFIS) is applied for the wind speed prediction by combining the obtained modes from VMD. Here, wind data of two sites in India, Jogimatti and Lamba are taken for the study. Each site data is grouped into high and low wind speed months and later, this series is decomposed into regular modes using VMD. Later, ANFIS is applied for training and predicting the wind speed for different time horizons. [ABSTRACT FROM AUTHOR]