Seyed M. Buhari

A Spectrum Switching Delay-Aware Scheduling Algorithm for Centralized Cognitive Radio Networks

We formulate a scheduling problem that takes into account different hardware delays experienced by the secondary users (SUs) in a centralized cognitive radio network (CRN) while switching to different frequency bands. We propose a polynomial-time suboptimal algorithm to address our formulated scheduling problem. We evaluate the impact of varying switching delay, number of frequencies, and number of SUs. Our simulation results indicate that our proposed algorithm is robust to changes in the hardware spectrum switching delay and its performance is very close to its upper bound. We also compare our proposed method with the corresponding constant switching delay-based algorithm and demonstrate that our suggestion of taking into account the different hardware delays while switching to different frequency bands is essential for scheduling in CRNs.

Automatic signature files update in antivirus software using active packets

The field of information technology is growing very quickly, and so there are more and more regular updates. At the same time, computer viruses are also on the increase, and people are now even complaining that attacks on computers are mostly from within the intranet structure. So, if we are able to update the signature files of the anti-virus software that exists on the various computers that are used by the various members of the locality concerned, we can prevent the problems of viruses to some extent. In order to do this, nowadays, system administrators send reminders to the people concerned, or else the computers have to be set to update every few days or so. These systems have their own drawbacks. To avoid this hazard, we provide an option for active packet-oriented automatic signature file updating.

Improved throughput for Power Line Communication (PLC) for smart meters using fog computing based data aggregation approach

The smart grid is now being deployed in many countries to provide cleaner and greener energy to the consumers. To implement this efficiently and effectively appropriate data collecting, processing and transmitting infrastructures are needed to be in place. Smart meters are used to measure the energy consumption details and patterns and transmit to meter data management system (MDMS) with the help of data aggregation units (DAU). The sheer amount of data need to be collected from the consumers are very huge and they fall under the category of big data. Currently, all the data collected from smart meters are stored in a centralized place for processing to forecast the energy demand. This approach is becoming a bottleneck for efficient data collection due to limited bandwidth capacities of Power Line Communication (PLC). In this paper, we propose a framework for distributed data aggregation approach with the help of fog computing architecture. With this approach the amount of data sent to the centralized storage space is limited and, therefore, the capacity of PLC is virtually improved without compromising the functionality.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

Abstract The operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

A cross-layer framework for sensor data aggregation for IoT applications in smart cities

Many Internet of Things (IoT) applications like smart parking, waste management, and traffic congestion management, are being developed for smart cities. These applications make use of billions of sensors which in turn generates a huge amount of data that comes under the category of Big Data. For IoT/Smart-city applications to make use of these data efficiently there needs to be a proper framework through which the required sensor could be easily searched and made use of. The existing Extract-Transformation-Loading (ETL) tools and other search mechanisms for sensors assume there exist registries where the sensors can be searched for the desired criteria through ontologies or other suitable techniques. However, there has not been enough contribution to efficiently retrieve the sensor data and to make it available in the required format for the registries to search for. In this paper, we analyze a distributed cross-layer commit protocol (CLCP) for data aggregations and its support for query based search for IoT application.

Resource management system for scientific virtual laboratory applications

The paper presents a conceptual framework for a resource management system designed for remote virtual laboratory experimentation in both the natural and physical sciences domains. One of the key problems addressed in this paper is the use of a mathematical model to solve resource allocation or task scheduling problems in a dynamic Grid environment that consists of a heterogeneous distributed cyberinfrastructure. The main focus of this paper however includes: architectural design model for scientific virtual laboratory tasks scheduling framework, resource allocation matchmaking algorithm design, mathematical modelling of resource allocation optimisation and computational analysis of the proposed system. The research work is in line with the on-going IT infrastructure networking project at the Ahmadu Bello University, Zaria, Nigeria.

Neural network‐based multi‐agent approach for scheduling in distributed systems

A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright

Scheduling multi-component applications with mobile agents in heterogeneous distributed systems

In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright

Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study

The presence of faulty valves has been studied in the literature with various machine learning approaches. The impact of using fault data only to train the system could solve the class imbalance problem in the machine learning approach. The data sets used for fault detection contain many independent variables, where the salient ones were selected using stepwise regression and applied to various machine learning techniques. A significant test for the given regression technique was used to validate the outcome. Machine learning techniques, such as decision trees and deep learning, are applied to the given data and the results reveal that the decision tree was able to obtain more than 95% accuracy and performed better than other algorithms when considering the tradeoff between the processing time and accuracy.

Feature selection algorithm for evoked EEG signal due to RGB colors

In this paper, a single trial classification is introduced for the Electroencephalography (EEG) signals evoked by RGB colors. The effectiveness of a single trial classification is an important step towards online classification of EEG signals. Signals are analyzed by Empirical Mode Decomposition (EMD) technique, and the last decomposition is used in the feature extraction stage. We investigate different feature extraction methods in order to find out the best method which can be used with colors dataset. These methods are: Event-Related Spectral Perturbations (ERSP), Target mean, AutoRegressive and EMD residual. In addition, we propose a new feature selection algorithm, which focuses on selecting the best features by studying the behavior of EEG components that appear due to the introduced color. We introduced a comparison between the classification results of using all extracted features, the results of using the selected features by the proposed algorithm and the results of using the selected features by recursive feature elimination algorithm, which is used by similar study. The proposed algorithm is proved with all the investigated feature extraction methods as the classification accuracies are increased. Support Vector Machine (SVM) is used in the classification process. We found that the execution time of using colors stimulus is only 0.23s, which is much less than the time which was required by any other stimulus such as imagery and spelling word presented in the previous researches. The best feature extraction method that gives the highest classification accuracy and can be used with real time BCI systems are Target Mean and EMD residual, as their accuracies are high and the computation time is very low.