With the advancement in network infrastructure and the growth of IoT, more and more vehicular systems are getting connected. Architectures like Information Centric Networks (ICN) are being explored for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error prone domains. Consumer mobility is implicitly supported in ICN while producer mobility is an important challenge. In this paper, we design a mechanism to support producer mobility using the spatial locality of moving producers and reverse paths of data. Then, we model the consumer (requesting) application as a GI/M/c/N queue which is used to estimate system parameters like content delivery time distribution. We perform simulations using real world traces to evaluate the accuracy and working of our model.

Many next generation Internet architectures exist in the literature for addressing various issues like increasing traffic, mobility and efficient content dissemination. One such emerging fundamental design is Information Centric Networking (ICN). The Pending Interest Table (PIT) is one of the essential components of the ICN forwarding plane responsible for the stateful routing in ICN. Optimal size of the PIT is essential for the efficient performance of the network and the enhanced consumer experience. Therefore, the optimal sizing of the PIT under various network conditions is an important and challenging problem. To this end, this paper models the PIT of a router as a GI/M/c/N queue. The model has (i) a general arrival process to accommodate the diverse nature of traffic, (ii) a service time model which takes into account the caching at the content stores and the mobility of producers, and (iii) a sojourn time distribution which is used to characterize the content delivery time at the consumers. Using the GI/M/c/N queueing model, we formulate an optimization problem to minimize the PIT size while subjecting the interest drop probability to an upper bound. The accuracy of our analytical model is demonstrated using simulations on different Internet Service Provider (ISP) topologies across a wide range of system parameters.

High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works only on discrete values. But most real-world datasets are continuous in nature. Use of traditional discernibility matrix approach inevitably incurs information loss due to discretization. In this paper, we propose a fuzzified adaptation of discernibility matrix with four variants of dissimilarity measure to deal with continuous data. The proposed algorithm has been applied on EEG dataset-III from BCI competition-II. The reduced dataset is then classified using Support Vector Machine (SVM). The performance of the proposed Fuzzy Discernibility Matrix (FDM) variants are compared with original discernibility matrix based method and Principal Component Analysis (PCA). In our empirical study, the proposed method outperforms the other two methods, thus suggesting that it is competitive with them.