Improving Energy Efficiency in MANETs by Multi-Path Routing

Some multi-path routing algorithm in MANET, simultaneously send information to the destination through several directions to reduce end-to-end delay. In all these algorithms, the sent traffic through a path affects the adjacent path and unintentionally increases the delay due to the use of adjacent paths. Because, there are repetitive competitions among neighboring nodes, in order to obtain the joint channel in adjacent paths. The represented algorithm in this study tries to discover the distinct paths between source and destination nodes with using Omni directional antennas, to send information through these simultaneously. For this purpose, the number of active neighbors is counted in each direction with using a strategy. These criterions are effectively used to select routes. Proposed algorithm is based on AODV routing algorithm, and in the end it is compared with AOMDV, AODVM, and IZM-DSR algorithms which are multi-path routing algorithms based on AODV and DSR. Simulation results show that using the proposed algorithm creates a significant improvement in energy efficiency and reducing end-to-end delay

On discretization of continuous attributes in Big Data mining

In the vast domain of data mining with many algorithms and methods, coping with continuous features in data sets is a common issue. Discretization is the process of converting these continuous attributes into discrete intervals. Most of the data mining algorithms expect the attributes to be categorical and/or discrete. And if they can handle continuous attributes, they are having lower accuracies in comparison with those that work with discrete and categorical attributes. Hence, discretization is a very important issue to be addressed. Discretization has also been referred to as a technique for data and noise reduction. There are several methods represented in the field of discretization but most of them are designed to work with small datasets. In this thesis, we have implemented and compared different distributed fuzzy discretizers, namely fuzzy MDLP and fuzzy ur-CAIM, using Map-Reduce programming paradigm and Apache Spark framework. We have analyzed the behavior of these discretizers using distributed fuzzy decision tree with 9 well-known big datasets. These distributed discretizers can be more efficient in handling big data sets. We have also compared the two discretizers using different fuzzy membership functions. The results of the discretizers are analyzed and the reasons behind their behavior are discussed in this thesis