This paper presents the free space optics (FSO) and radio frequency (RF) wireless communication. The paper explains the feature of FSO and compares it with the already deployed technology of RF communication in terms of data rate, efficiency, capacity and limitations. The data security is also discussed in the paper for identification of the system to be able to use in normal circumstances. These systems are also discussed in a way that they could efficiently combine to form the single system with greater throughput and higher reliability.

Data-warehouse is an emerging technology with great potential. Nowadays, businesses are competing fiercely to dominate the market where profitability is promising using every available means to reach their goal. Performance and storage are big challenges in building data-warehouse focusing by researchers recent years. In this paper a new model for developing an efficient data warehouse by using mobile agent technology has been proposed. The main idea behind this model is to use the mobile agent to extract and analyze operational data in their location. So, instead of using ETL, the mobile agent will be used. After the mobile agent completing its journey among operational databases, all tasks of ETL will be performed. By this way no need high storage media to extract the data from the operational database. As cost of time, the model proves less consuming of time. The model has been implemented using .Net Framework and C# and the results have been presented and discussed.

In this paper, a comparative study of Wideband MUSIC method, Joint Diagonalization Structure (JDS) method and Beam-Space Genetic Algorithm (BGA) is presented with respect to resolution probability and Root mean square error (RMSE) evaluated in both low and high Signal-to-Noise ratio (SNR) regions. The simulation results show that BGA has higher resolution probability than the JDS method and Wideband MUSIC method in low SNR region while in the high SNR region, Wideband MUSIC has higher resolution probability followed by BGA and JDS method. RMSE is smallest for BGA as compared to JDS and Wideband MUSIC in low SNR region. In the high SNR region, Wideband MUSIC has smallest RMSE followed by BGA and JDS method.

Since various pseudo-random algorithms and sequences are used for cryptography of data or as initial values for starting a secure communication, how these algorithms are analyzed and selected is very important. In fact, given the growingly extensive types of pseudo-random sequences and block and stream cipher algorithms, selection of an appropriate algorithm needs an accurate and thorough investigation. Also, in order to generate a pseudo-random sequence and generalize it to a cryptographer algorithm, a comprehensive and regular framework is needed, so that we are enabled to evaluate the presented algorithm as quick as possible. The purpose of this study is to use a number of pseudo-random number generators as well as popular cryptography algorithms, analyze them in a standard framework and observe the results obtained in each stage. The investigations are like a match between different algorithms, such that in each stage, weak algorithms are eliminated using a standard method and successful algorithms enter the next stage so that the best algorithms are chosen in the final stage. The main purpose of this paper is to certify the approved algorithm.

Text Classification is done mainly through classifiers proposed over the years, Naïve Bayes and Maximum Entropy being the most popular of all. However, the individual classifiers show limited applicability according to their respective domains and scopes. Recent research works evaluated that the combination of classifiers when used for classification showed better performance than the individual ones. This work introduces a modified Maximum Entropy-based classifier. Maximum Entropy classifiers provide a great deal of flexibility for parameter definitions and follow assumptions closer to real world scenario. This classifier is then combined with a Naïve Bayes classifier. Naïve Bayes Classification is a very simple and fast technique. The assumption model is opposite to that of Maximum Entropy. The combination of classifiers is done through operators that linearly combine the results of two classifiers to predict class of documents in query. Proper validation of the 7 proposed modifications (4 modifications of Maximum Entropy, 3 combined classifiers) are demonstrated through implementation and experimenting on real life datasets.

A multi-agent system (MAS) is formed by a number of agents connected together to achieve the desired goals specified by the design. Usually in a multi agent system, agents work on behalf of a user to accomplish given goals. In MAS co-ordination, co-operation, negotiation and communication are important aspects to achieve fault tolerance in MAS. The multi-agent system is likely to fail in a distributed environment and as an outcome of such, the resources for MAS may not be available due to the failure of an agent, machine crashes, process failure, software failure, communication failure and/or hardware failure. Therefore, many researchers have proposed fault tolerance approaches to overcome the failure in MAS. So we have surveyed these approaches in this paper, whereby our contribution is threefold. Firstly, we have provided taxonomy of faults and techniques in MAS. Secondly, we have provided a qualitative comparison of existing fault tolerance approaches. Thirdly, we have provided an evaluation of existing fault tolerance techniques. Results show that most of the existing schemes are not very efficient, due to various reasons like high computation costs, costly replication and large communication overheads.

This Software defined networks helps to realize extraordinary services that can be easily embedded in network operations of switch. It provokes the decomposition of the control and data planes. The control plane is more extensible, as it is unproblematic to change or introduce any new functionality into the network. It is studied that any new integration can be easily added up with a very low line of code (LOC). The work proposes a fuzzy based approach for traffic provisioning in SDN. Fuzzy Logic Control System (FLCS) is a controller comprising of two fuzzy systems- Label Switched Path setup System (LsS) and Traffic Splitting System (TSS). The computation of dynamic status of Load and Delay is utilized by LsS to arrange the paths in preference order. The attained Link Capacity and Utilization Rate are employing by TSS for maintaining congestion free path. Created three different topologies and performed ping reachability test and executed iperf testing tool for performance analysis on Mininet framework.The impact of this is to facilitate better decision making for splitting the traffic for different capable paths. Simulation setup is deployed using OpenFlow Switches and Controllers to study their performance. The packet delivery ratio remained above 98%, showing rare chances of congestion and delay was below than 2.6 seconds with TTL in range of 60-80 milliseconds.

In this paper, we propose a 3-level heterogeneous network model for WSNs to enhance the network lifetime, which is characterized by a single parameter. Depending upon the value of the model parameter, it can describe 1-level, 2-level, and 3-level heterogeneity. Our heterogeneous network model also helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function. We compute the network lifetime by implementing HEED protocol for our network model. The HEED implementation for the existing 1-level, 2-level, and 3-level heterogeneous network models are denoted as HEED-1, HEED-2, and HEED-3, respectively, and for our proposed 3-level heterogeneous network model, the SEP implementations are denoted as hetHEED-1, hetHEED-2, and hetHEED-3, respectively. As evident from the simulation results, the hetHEED-3 provides longer lifetime than that of the HEED-3 for all cases.

The explosive rate of increase in number of habitats and vehicles in different areas of the developing countries like Nigeria has motivated government of such world engage in both rural and urban road construction for ease of navigation. This brings stresses in navigating such roads with public traffic hence noise pollution to the environment. For effective autonomous geo-spatial navigation service, we propose a web based model implemented as intelligent mobile application for route finding and transport cost analysis. A case study observed on data collated from different areas within Ile-Ife and its surroundings shows that the system aid users in making decision regarding transportation alternatives. This study shows how to help people living in such parts of the world reach their destinations when navigating unknown routes with reduced transportation cost.

Large amount of data that is characterized by its volume, velocity, veracity, value and variety is termed Big Data. Extracting hidden patterns, customer preferences, market trends, unknown correlations, or any other useful business information from large collection of structured or unstructured data set is called Big Data analysis. This article explores the scope of analyzing bank transaction data to categorize customers which could help the bank in efficient marketing, improved customer service, better operational efficiency, increased profit and many other hidden benefits. Instead of relying on a single technology to process large scale data, we make use of a combination of strategies like Hadoop, PIG, R etc for efficient analysis. RHadoop is an upcoming research trend for Big Data analysis, as R is a very efficient and easy to code, data analysis and visualization tool compared to traditional MapReduce program. K-Means is chosen as the clustering algorithm for classification.