Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
12 results
Search Results
Conference Object Citation - WoS: 30Citation - Scopus: 38Software Defined Communication Framework for Smart Grid to Meet Energy Demands in Smart Cities(IEEE, 2019-04) Faheem, Muhammad; Umar, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Ngadi, Md. Asri; Gungor, Vehbi CagriIn smart cities, the electricity is an essential component since it preserves a certain level of residents' life quality and provisions the entire spectrum of their economic activities. Thus, a smart way is essential to develop cities without disregarding energy issues. In this scope, the smart grid paradigm offers power supply in an efficient, sustainable and economical manner with minimal impact on the environment and can meet the future energy demands. However, real-time monitoring and control of the smart grid (SG) for continuous and quality-aware power supply in smart cities (SCs) is challenging and requires an advanced quality of service (QoS)-aware communication framework. In this context, this research aims to present a novel data-gathering scheme by using the Internet of software-defined mobile sinks (SDMSs) and wireless sensor networks (WSNs) in the smart grid. The extensive simulation results conducted through the EstiNet9.0 indicate that the designed scheme outperforms existing approaches and achieves its defined goals for events-drive applications in the SG.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 - 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.Conference Object Citation - WoS: 7Citation - Scopus: 12Ambient Energy Harvesting for Low Powered Wireless Sensor Network Based Smart Grid Applications(Institute of Electrical and Electronics Engineers Inc., 2019-04) Faheem, Muhammed Yasir; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Raza, Basit; Ngadi, M. A.; Güngör, Vehbi ÇağrıLimited battery lifetime is one of the most critical issues for wireless sensor networks (WSNs)-based smart grid (SG) applications. Recently, ambient energy harvesting (AEH) has been considered to significantly improve the network lifetime of the WSNs-based SG applications. However, extracting a significant amount of energy from the ambient energy resource due to time varying links quality affected by power grid environments is the main issue for WSNs-based applications in SG. In this paper, we propose a novel multi-source energy harvesting mechanisms for WSNs-based SG applications. The propose hybrid ambient energy harvesting framework through the designed circuitry successfully harvests massive power density by capturing the radial electric field (EF) and ambient radio frequency WiFi 2.4GHz band signals present in the vicinity of 500kV power grid station. The design energy harvesting schemes have been implemented on the recently developed routing protocol for SG applications. The experiments using EstiNet9.0, demonstrate that the designed framework is efficient in terms of energy harvesting capabilities to enable a long-lasting lifetime of the WSNs-based smart grid applications. © 2020 Elsevier B.V., All rights reserved.Article Citation - WoS: 33Citation - Scopus: 40A Multi-Channel Distributed Routing Scheme for Smart Grid Real-Time Critical Event Monitoring Applications in the Perspective of Industry 4.0(Inderscience Publishers, 2019) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıRecently, the 4th industrial revolution known as Industry 4.0 has paved way for a systematical deployment of the modernised power grid to fulfil the continuously growing energy demand of the 21st century. This paper proposes a novel channel-aware distributed routing protocol named CARP for CRSNs-based SG applications. In CARP, the proposed cooperative channel assignment mechanism significantly improves the detection reliability and mitigates the noise and congested spectrum bands resulting in reliable and high capacity links for CRSNs-based SG applications. Moreover, to support higher capacity data requirements and to maximise the spectrum utilisation, the proposed multi-hop routing mechanism selects a secondary user relay node rich in spectrum information with longer ideal probability at low interference in the network. The extensive simulation results conducted through EstiNet9.0 reveal that the proposed scheme achieves its defined goals compared to existing routing schemes designed for CRSNs-based applications. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3A Hybrid Adaptive Neuro-Fuzzy Inference System (Anfis) Approach for Professional Bloggers Classification(IEEE, 2019-11) Asim, Yousra; Raza, Basit; Malik, Ahmad Kamran; Shahid, Ahmad R.; Faheem, Muhammad; Kumar, Yogan JayaDespite their small numbers, some users of the online social networks demonstrate the ability to influence others. Bloggers are one of such kind of users that through their ideas and opinions on different topics, influence other users. Their identification may be beneficial for several purposes, such as online marketing for products. Much effort has been expanded towards finding the impact of such bloggers within the blogging community. We have expanded on their work by identifying influential bloggers using labeled data. We have improved upon the accuracy of the classification of professional and non-professional bloggers. We have made use of Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Fuzzy Inference System (FIS) models. Their performance has been gauged and compared with the existing techniques and approaches, such as an Artificial Neural Network (ANN), Alternating Decision Tree (ADTree) algorithm, and Classification Based on Associations (CBA) algorithm. Adaptive techniques (ANFIS and ANN) are found better than the aforementioned rule-based classifiers. The FIS model outperformed the CBA algorithm, but showed similar performance to the ADTree algorithm. Our proposed ANFIS model showed improved results in terms of performance measures with 93% accuracy for blogger classification.