Browsing by Author "Faheem, Muhammad"

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Ambient Energy Harvesting for Low Powered Wireless Sensor Network based Smart Grid Applications
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Faheem, Muhammad; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Raza, Basit; Ngadi, Md. Asri; Gungor, Vehbi Cagri; 0000-0003-4907-6359; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Limited 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.
Autonomic performance prediction framework for data warehouse queries using lazy learning approach
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Raza, Basit; Aslam, Adeel; Sher, Asma; Malik, Ahmad Kamran; Faheem, Muhammad; 0000-0001-6711-2363; 0000-0001-5569-5629; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Information 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.
Autonomic workload performance tuning in large-scale data repositories
(SPRINGER LONDON LTD, 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND, 2019) Raza, Basit; Sher, Asma; Afzal, Sana; Malik, Ahmad Kamran; Anjum, Adeel; Kumar, Yogan Jaya; Faheem, Muhammad; 0000-0002-2024-0699; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
The 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.
Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Faheem, Muhammad; Fizza, Ghulam; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Ngadi, Md. Asri; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad; Gungor, Vehbi Cagri
Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyberphysical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) re-quirements in the smart grid. In this context, this paper de-scribes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assign-ment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid. (C) 2021 The Authors. Published by Elsevier Inc.
Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0
(ELSEVIER, 2022) Faheem, Muhammad; Butt, Rizwan Aslam; 0000-0003-4628-4486; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad
The Industry 4.0 revolution is aimed to optimize the prod- uct design according to the customers’ demand, quality re- quirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimiz- ing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service per- formance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical- Wireless Sensor Networks (OWSNs) for optimizing service systems’ performance in the electronics manufacturing In- dustry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data de- livery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.
Capacity and spectrum-aware communication framework for wireless sensor network-based smart grid applications
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2017) Faheem, Muhammad; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Recently, wireless sensor networks (WSNs) have been widely recognized as a promising technology for enhancing various aspects of smart grid and realizing the vision of next-generation electric power system in a cost-effective and efficient manner. However, recent field tests show that wireless links in smart grid environments have higher packet error rates and variable link capacity because of dynamic topology changes, obstructions, electromagnetic interference, equipment noise, multipath effects, and fading. To overcome these communication challenges, in this paper, we propose a data capacity-aware channel assignment (DCA) and fish bone routing (FBR) algorithm for WSN-based smart grid applications. The proposed DCA framework deals with the channel scarcities by dynamically switching between different spectrum bands and employs a network for organizing WSN into a highly stable connected hierarchy. In addition, the proposed FBR mechanism provides robust loop free data paths and avoids high transmission cost, excessive end-to-end delay and restricts unnecessary multi-hop data transmission from the source to destination in the network. Thus, it significantly reduces the probability of data packet loss and preserves stable link qualities among sensor nodes for load balancing and prolonging the lifetime of wireless sensor networks in harsh smart grid environments. Comparative performance evaluations show that our proposed schemes outperform the existing communication architectures in terms of data packet delivery, communication delay and energy consumption.
CBI4.0: A cross-layer approach for big data gathering for active monitoring and maintenance in the manufacturing industry 4.0
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad; Gungor, Vehbi Cagri
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.
Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2018) Ashraf, Muhammad Waqar; Idrus, Sevia M.; Iqbal, Farabi; Butt, Rizwan Aslam; Faheem, Muhammad; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü;
Network survivability endeavors to ensure the uninterrupted provisioning of services by the network operators in case of a disaster event. Studies and news reports show that network failures caused by physical attacks and natural disasters have significant impacts on the optical networks. Such network failures may lead to a section of a network to cease to function, resulting in non-availability of services and may increase the congestion within the rest of the network. Therefore, fault tolerant and disaster-resilient optical networks have grasped the attention of the research community and have been a critical concern in network studies during the last decade. Several studies on protection and restoration techniques have been conducted to address the network component failures. This study reviews related previous research studies to critically discuss the issues regarding protection, restoration, cascading failures, disaster-based failures, and congestion-aware routing. We have also focused on the problem of simultaneous cascading failures (which may disturb the data traffic within a layer or disrupt the services at upper layers) along with their mitigating techniques, and disaster-aware network survivability. Since traffic floods and network congestion are pertinent problems, they have therefore been discussed in a separate section. In the end, we have highlighted some open issues in the disaster-resilient network survivability for research challenges and discussed them along with their possible solutions.
EDHRP: Energy efficient event driven hybrid routing protocol for densely deployed wireless sensor networks
(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2015) Faheem, Muhammad; Abbas, Muhammad Zahid; Tuna, Gurkan; Gungor, Vehbi Cagri; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad; Gungor, Vehbi Cagri
Efficient management of energy resources is a challenging research area in Wireless Sensor Networks (WSNs). Recent studies have revealed that clustering is an efficient topology control approach for organizing a network into a connected hierarchy which balances the traffic load of the sensor nodes and improves the overall scalability and the lifetime of WSNs. Inspired by the advantages of clustering techniques, we have three main contributions in this paper. First, we propose an energy efficient cluster formation algorithm called Active Node Cluster Formation (ANCF). The core aim to propose ANCF algorithm is to distribute heavy data traffic and high energy consumption load evenly in the network by offering unequal size of clusters in the network. The developed scheme appoints each cluster head (CH) near to the sink and sensing event while the remaining set of the cluster heads (CHs) are appointed in the middle of each cluster to achieve the highest level of energy efficiency in dense deployment. Second, we propose a lightweight sensing mechanism called Active Node Sensing Algorithm (ANSA). The key aim to propose the ANSA algorithm is to avoid high sensing overlapping data redundancy by appointing a set of active nodes in each cluster with satisfy coverage near to the event. Third, we propose an Active Node Routing Algorithm (ANRA) to address complex inter and intra cluster routing issues in highly dense deployment based on the node dominating values. Extensive experimental studies conducted through network simulator NCTUNs 6.0 reveal that our proposed scheme outperforms existing routing techniques in terms of energy efficiency, end-to-end delay and data redundancy, congestion management and setup robustness.
Energy efficient multi-objective evolutionary routing scheme for reliable data gathering in Internet of underwater acoustic sensor networks
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2019) Faheem, Muhammad; Ngadi, Asri; Gungor, Vehbi Cagri; 0000-0003-4907-6359; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Earth's surface is covered with two-thirds of water. The marine world covers the lakes, rivers and sea and is rich in natural resources largely unexplored by human beings. Recently, underwater wireless sensor network (UWSN) with the advancement in the Internet of underwater smart things has emerged as promising networking techniques to explore the mysteries of vastly unexplored ocean environments for several underwater applications. These applications include offshore exploration, pollution monitoring, disaster prevention, oceanographic data collection, offshore oil fields monitoring, tactical surveillance applications and several others. However, the underwater channel impairments caused by multipath effects, fading, bit errors, variable and high latency and low bandwidth severely limits the data transmission reliability for UWSNs-based applications. This results in poor quality-aware data gathering in UWSNs. Therefore, designing a quality of service (QoS)-aware data gathering protocol to monitor and explore oceans is challenging in the underwater environments. In this paper, we propose a bio-inspired multi-objective evolutionary routing protocol (called MERP) for UWSNs-based applications. The designed routing protocol exploits the features of the natural evolution of the multi-objective genetic algorithm in order to provide reliable and energy-aware information gathering in UWSNs. The extensive simulation results show that the developed protocol attains its defined goals compared to existing UWSNs-based routing protocols during monitoring and exploring underwater environments. (C) 2019 Elsevier B.V. All rights reserved.
Enhanced Energy Savings with Adaptive Watchful Sleep Mode for Next Generation Passive Optical Network
(MDPI, 2022) Butt, Rizwan Aslam; Akhunzada, Adnan; Faheem, Muhammad; Raza, Basit; 0000-0002-4784-0918; 0000-0001-8370-9290; 0000-0001-6711-2363; 0000-0003-4628-4486; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad
A 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.
FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2020) Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Alquhayz, Hani; Ashraf, Muhammad Waqar; Raza, Saleem; Bin Ngadi, Md Asri; 0000-0003-1591-7041; 0000-0003-4907-6359; 0000-0002-4784-0918; 0000-0003-4628-4486; 0000-0001-6711-2363; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Energy-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.
Handling incomplete data classification using imputed feature selected bagging (IFBag) method
(IOS PRESSNIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS, 2021) Khan, Ahmad Jaffar; Raza, Basit; Shahid, Ahmad Raza; Kumar, Yogan Jaya; Faheem, Muhammad; Alquhayz, Hani; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Kumar, Yogan Jaya; Faheem, Muhammad
Almost 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.
A Hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach for Professional Bloggers Classification
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Asim, Yousra; Raza, Basit; Malik, Ahmad Kamran; Shahid, Ahmad R.; Faheem, Muhammad; Kumar, Yogan Jaya; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Despite 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.
Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey
(John Wiley and Sons Ltd, 2019) Raza, Saleem; Faheem, Muhammad; Gunes, Mesut; 0000-0003-4628-4486; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad
ThevisiontoconnecteverydayphysicalobjectstotheInternetpromisestocreate the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. Inthisarticle,weexploreIWSAN,itsapplications,requirements,challenges, andsolutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards andtechnologies. A numberofapplication-specific MACprotocols developedto support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, weemphasize on existing challenges and present important future directions.
LRP: Link quality-aware queue-based spectral clustering routing protocol for underwater acoustic sensor networks
(WILEY111 RIVER ST, HOBOKEN 07030-5774, NJ, 2017) Faheem, Muhammad; Tuna, Gurkan; Gungor, Vehbi Cagri; 0000-0002-6466-4696; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Recently, underwater acoustic sensor networks (UASNs) have been considered as a promising approach for monitoring and exploring the oceans in lieu of traditional underwater wireline instruments. As a result, a broad range of applications exists ranging from oil industry to aquaculture and includes oceanographic data collection, disaster prevention, offshore exploration, assisted navigation, tactical surveillance, and pollution monitoring. However, the unique characteristics of underwater acoustic communication channels, such as high bit error rate, limited bandwidth, and variable delay, lead to a large number of packet drops, low throughput, and significant waste of energy because of packets retransmission in these applications. Hence, designing an efficient and reliable data communication protocol between sensor nodes and the sink is crucial for successful data transmission in underwater applications. Accordingly, this paper is intended to introduce a novel nature-inspired evolutionary link quality-aware queue-based spectral clustering routing protocol for UASN-based underwater applications. Because of its distributed nature, link quality-aware queue-based spectral clustering routing protocol successfully distributes network data traffic load evenly in harsh underwater environments and avoids hotspot problems that occur near the sink. In addition, because of its double check mechanism for signal to noise ratio and Euclidean distance, it adopts opportunistically and provides reliable dynamic cluster-based routing architecture in the entire network. To sum up, the proposed approach successfully finds the best forwarding relay node for data transmission and avoids path loops and packet losses in both sparse and densely deployed UASNs. Our experimental results obtained in a set of extensive simulation studies verify that the proposed protocol performs better than the existing routing protocols in terms of data delivery ratio, overall network throughput, end-to-end delay, and energy efficiency.
MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things
(ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2018) Faheem, Muhammad; Gungor, V. C.; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
The recent advances in internet of things (IoT) and industrial wireless sensor networks (IWSNs) paradigm provide a promising opportunity for upgrading todays elderly electricity industrial systems and even allow the fourth stage of the industrial revolution, referred to as smart grid industry (SGI) 4.0. In SGI 4.0 paradigm, the WSNs are considered as promising solutions due to their advantages, such as cable replacement, ease of deployment, flexibility, and cost reduction. However, harsh and complex smart grid (SG) environments pose great challenges to guarantee reliable communication for WSNs-based SG applications due to equipment noise, electromagnetic interference, multipath effects and fading in SG environments. This results in deteriorating the quality-of-service (QoS) requirements as well as the network lifetime of multi-hop communication-based WSNs for SG applications. Thus, for SGI 4.0 paradigm to come true, a WSN-based highly reliable communication infrastructure is crucial that will wirelessly connect and integrate power system components for more efficient, reliable, and intelligent operations of the next-generation electricity power grids. To address these challenges, in this paper a novel multi-mobile sinks-based QoS-aware data gathering protocol (called MQRP) for WSNs-based SG applications has been proposed to empower SGI 4.0. The extensive simulations study is carried through a network simulation tool called EstiNet9.0. The obtained experimental facts show that the proposed scheme has not only improved the QoS performance metrics, such as packet delivery ratio, memory utilization, control message overhead, residual energy, network lifetime, and throughput, but also reduced packet error rate and end-to-end delay compared to existing data collection schemes. (C) 2017 Elsevier B.V. All rights reserved.
A multi-channel distributed routing scheme for smart grid real-time critical event monitoring applications in the perspective of Industry 4.0
(INDERSCIENCE ENTERPRISES LTD, WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND, 2019) Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, Md A.); Gungor, Vehbi Cagri; 0000-0003-4907-6359; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Recently, 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.
A multiobjective, lion mating optimization inspired routing protocol for wireless body area sensor network based healthcare applications
(MDPI AG, 2019) Faheem, Muhammad; Aslam Butt, Rizwan; Raza, B.; Alquhayz, Hani; Abbas, Muhammad Zahid; Ngadi, M. A.; Güngör, Vehbi Çağrı; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü;
The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach
(PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND, 2018) Raza, Basit; Kumar, Yogan Jaya; Malik, Ahmad Kamran; Anjum, Adeel; Faheem, Muhammad; 0000-0002-2024-0699; 0000-0003-4282-1010; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Workload 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.