Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 31Citation - Scopus: 41QoSRP: A Cross-Layer QoS Channel-Aware Routing Protocol for the Internet of Underwater Acoustic Sensor Networks(MDPI, 2019-11-02) Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Alquhayz, Hani; Ashraf, Muhammad Waqar; Shah, Syed Bilal; Gungor, Vehbi CagriQuality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.Article Citation - WoS: 37Citation - Scopus: 35Performance Prediction and Adaptation for Database Management System Workload Using Case-Based Reasoning Approach(Pergamon-Elsevier Science Ltd, 2018-07) Raza, Basit; Kumar, Yogan Jaya; Malik, Ahmad Kamran; Anjum, Adeel; Faheem, MuhammadWorkload management in a Database Management System (DBMS) has become difficult and challenging because of workload complexity and heterogeneity. During and after execution of the workload, it is hard to control and handle the workload. Before executing the workload, predicting its performance can help us in workload management. By knowing the type of workload in advance, we can predict its performance in an adaptive way that will enable us to monitor and control the workload, which ultimately leads to performance tuning of the DBMS. This study proposes a predictive and adaptive framework named as the Autonomic Workload Performance Prediction (AWPP) framework. The proposed AWPP framework predicts and adapts the DBMS workload performance on the basis of information available in advance before executing the workload. The Case-Based Reasoning (CBR) approach is used to solve the workload management problem. The proposed CBR approach is compared with other machine learning techniques. To validate the AWPP framework, a number of benchmark workloads of the Decision Support System (DSS) and the Online Transaction Processing (OLTP) are executed on the MySQL DBMS. For preparation of training and testing data, we executed more than 1000 TPC-H and TPC-C like workloads on a standard data set. The results show that our proposed AWPP framework through CBR modeling performs better in predicting and adapting the DBMS workload. DBMSs algorithms can be optimized for this prediction and workload can be controlled and managed in a better way. In the end, the results are validated by performing post-hoc tests. (C) 2018 Elsevier Ltd. All rights reserved.Article Handling Incomplete Data Classification Using Imputed Feature Selected Bagging (IFBAG) Method(Ios Press, 2021-07-09) Khan, Ahmad Jaffar; Raza, Basit; Shahid, Ahmad Raza; Kumar, Yogan Jaya; Faheem, Muhammad; Alquhayz, HaniAlmost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.Article Citation - WoS: 54Citation - Scopus: 68FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Alquhayz, Hani; Ashraf, Muhammad Waqar; Raza, Saleem; Bin Ngadi, Md Asri; Ngadi, Md. Asri BinEnergy-efficient and reliable data gathering using highly stable links in underwater wireless sensor networks (UWSNs) is challenging because of time and location-dependent communication characteristics of the acoustic channel. In this paper, we propose a novel dynamic firefly mating optimization inspired routing scheme called FFRP for the internet of UWSNs-based events monitoring applications. The proposed FFRP scheme during the events data gathering employs a self-learning based dynamic firefly mating optimization intelligence to find the highly stable and reliable routing paths to route packets around connectivity voids and shadow zones in UWSNs. The proposed scheme during conveying information minimizes the high energy consumption and latency issues by balancing the data traffic load evenly in a large-scale network. In additions, the data transmission over highly stable links between acoustic nodes increases the overall packets delivery ratio and network throughput in UWSNs. Several simulation experiments are carried out to verify the effectiveness of the proposed scheme against the existing schemes through NS2 and AquaSim 2.0 in UWSNs. The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs.Article Citation - WoS: 4Citation - Scopus: 6Enhanced Energy Savings With Adaptive Watchful Sleep Mode for Next Generation Passive Optical Network(MDPI, 2022-02-23) Butt, Rizwan Aslam; Akhunzada, Adnan; Faheem, Muhammad; Raza, BasitA single watchful sleep mode (WSM) combines the features of both cyclic sleep mode (CSM) and cyclic doze mode (CDM) in a single process by periodically turning ON and OFF the optical receiver (RX) of the optical network terminal (ONT) in a symmetric manner. This results in almost the same energy savings for the ONTs as achieved by the CSM process while significantly reducing the upstream delays. However, in this study we argue that the periodic ON and OFF periods of the ONT RX is not an energy efficient approach, as it reduces the ONT Asleep (AS) state time. Instead, this study proposes an adaptive watchful sleep mode (AWSM) in which the RX ON time of ONT is minimized during ONT Watch state by choosing it according to the length of the traffic queue of the type 1 (T1) traffic class. The performance of AWSM is compared with standard WSM and CSM schemes. The investigation reveals that by minimizing the RX ON time, the AWSM scheme achieves up to 71% average energy saving per ONT at low traffic loads. The comparative study results show that the ONT energy savings achieved by AWSM are 9% higher than the symmetric WSM with almost the same delay and delay variance performance.Article Citation - Scopus: 63Energy Efficient and Reliable Data Gathering Using Internet of Software-Defined Mobile Sinks for WSNS-Based Smart Grid Applications(Elsevier B.V., 2019-10) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıThe smart grid is an emerging concept that introduces innovative ways to handle the power quality and reliability issues for both service provider and consumers. The key aims of the smart grid (SG) in smart cities (SCs) is to preserve a certain level of residents’ life quality and support the entire spectrum of their economic activities. In this paper, we present a novel Energy Efficient and Reliable Data Gathering Routing Protocol (ODGRP) for wireless sensor networks (WSNs)-based smart grid applications. The developed scheme employs a software-defined centralized controller and multiple mobile sinks for energy efficient and reliable data gathering from WSNs in the SG. The extensive simulation results conducted through the EstiNet 9.0 show that the designed scheme outperforms existing approaches and achieves its defined goals for event-driven applications in the SG. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 25Citation - Scopus: 31Autonomic Performance Prediction Framework for Data Warehouse Queries Using Lazy Learning Approach(Elsevier, 2020-06) Raza, Basit; Aslam, Adeel; Sher, Asma; Malik, Ahmad Kamran; Faheem, MuhammadInformation is one of the most important assets of an organization. In recent years, the volume of data stored in organizations, varying user requirements, time constraints, and query management complexities have grown exponentially. Due to these problems, the performance modeling of queries in data warehouses (DWs) has assumed a key role in organizations. DWs make relevant information available to decision-makers; however, DW administration is becoming increasingly difficult and time-consuming. DW administrators spend too much time managing queries, which also affects data warehouse performance. To enhance the performance of overloaded data warehouses with varying queries, a prediction-based framework is required that forecasts the behavior of query performance metrics in a DW. In this study, we propose a cluster-based autonomic performance prediction framework using a case-based reasoning approach that determines the performance metrics of the data warehouse in advance by incorporating autonomic computing characteristics. This prediction is helpful for query monitoring and management. For evaluation, we used metrics for precision, recall, accuracy, and relative error rate. The proposed approach is also compared with existing lazy learning techniques. We used the standard TPC-H dataset. Experiments show that our proposed approach produce better results compared to existing techniques. (C) 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 7Citation - Scopus: 8Autonomic Workload Performance Tuning in Large-Scale Data Repositories(Springer London Ltd, 2018-09-04) Raza, Basit; Sher, Asma; Afzal, Sana; Malik, Ahmad Kamran; Anjum, Adeel; Kumar, Yogan Jaya; Faheem, MuhammadThe workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.Article Citation - Scopus: 3Attack-Aware Dynamic Upstream Bandwidth Assignment Scheme for Passive Optical Network(Walter de Gruyter GmbH, 2019-09-19) Butt, Rizwan Aslam; Faheem, Muhammed Yasir; Ashraf, Muhammad Waqar; Khawaja, Attaullah; Raza, BasitNetwork security is an important component of today's networks to combat the security attacks. The passive optical network (PON) works at the medium access layer (MAC). A distributed denial of service (DDOS) attack may be launched from the network and transport layers of an Optical Network unit (ONU). Although there are various security techniques to mitigate its impact, however, these techniques cannot mitigate the impact on the MAC Layer of the PON and can cause an ONU to continuously drain too much bandwidth. This will result in reduced bandwidth availability to other ONUs and, thus, causing an increase in US delays and delay variance. In this work we argue that the impact of a DDOS attack can be mitigated by improving the Dynamic bandwidth assignment (DBA) scheme which is used in PON to manage the US bandwidth at the optical line terminal (OLT). The present DBA schemes do not have the capability to combat a security attack. Thus, this study, uses a machine learning approach to learn the ONU traffic demand patterns and presents a security aware DBA (SA-DBA) scheme that detects a rogue (attacker) ONU from its traffic demand pattern and limits its illegitimate bandwidth demand and only allows it the bandwidth assignment to it as per the agreed service level agreement (SLA). The simulation results show that the SA-DBA scheme results in up to 53%, 55% and 90% reduced US delays and up to 84%, 76% and 95% reduced US delay variance of T2, T3 and T4 traffic classes compared to existing insecure DBA schemes. © 2023 Elsevier B.V., All rights reserved.Article Citation - Scopus: 98An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo-Tompa and Stacked Genetic Algorithm(Institute of Electrical and Electronics Engineers Inc., 2020) Ali, Syed Arslan; Raza, Basit; Malik, Ahmad Kamran Kamran; Shahid, Ahmad Raza; Faheem, Muhammed Yasir; Alquhayz, Hani Ali; Kumar, Y. J.A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%. © 2020 Elsevier B.V., All rights reserved.