To obtain high performance, generalization, and accuracy in machine learning applications, such as prediction or anomaly detection, large datasets are a necessary prerequisite. Moreover, the collection of data is time-consuming, difficult, and expensive for many imbalanced or small datasets. These challenges are evident in collecting data for financial and banking services, pharmaceuticals and healthcare, manufacturing and the automobile, robotics car, sensor time-series data, and many more. To overcome the challenges of data collection, researchers in many domains are becoming more and more interested in the development or generation of synthetic data. Generating synthetic time-series data is far more complicated and expensive than generating synthetic tabular data. The primary objective of the paper is to generate multivariate time-series data (for continuous and mixed parameters) that are comparable and evaluated with real multivariate time-series synthetic data. After being trained to produce such data, a novel GAN architecture named as MTS-TGAN is proposed and then assessed using both qualitative measures namely t-SNE, PCA, discriminative and predictive scores as well as quantitative measures, for which an RNN model is implemented, which calculates MAE and MSLE scores for three training phases; Train Real Test Real, Train Real Test Synthetic and Train Synthetic Test Real. The model is able to reduce the overall error up to 13% and 10% in predictive and discriminative scores, respectively. The research’s objectives are met, and the outcomes demonstrate that MTS-TGAN is able to pick up on the distribution and underlying knowledge included in the attributes of the real data and it can serve as a starting point for additional research in the respective area.

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In Mobile ad hoc networks (MANETs), mobile nodes communicate with each other with the help of hello messages as there is no centralized infrastructure. Routing protocols in MANETs are mainly concerned with unicasting and multicasting. Design of an efficient multicast routing protocol is one of the major challenges of MANETs. Multicast routing protocol deals with the transfer of messages from a source to multiple destinations and it has become very popular nowadays. In this paper, we propose a multicast routing protocol (DA-MRP) for MANETs that includes the directional forwarding along largest distance from source to destinations. We analyze the performance and efficiency of our proposed protocol using simulator OMNET++. Our protocol aims at achieving reduced packet overhead, low network delay and high scalability in the network. Our protocol is mainly proved best in dense network.

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