Harun Jamil - Academia.edu (original) (raw)
Papers by Harun Jamil
IEEE Access, 2022
The scientific community believes that peer-to-peer energy trading will dominate a significant po... more The scientific community believes that peer-to-peer energy trading will dominate a significant portion of forthcoming power generation systems research. Despite a plethora of optimal energy trading solutions, optimizing the trading cost and intelligent formation of energy sharing strategies are deemed exigent problems. Contemplating the excessive rise of energy demands across the globe, this study introduces a predictive optimization-based nanogrid energy trading model that minimizes energy trading cost and provides an optimal energy sharing plan between peers connected within a nanogrid network/cluster. The proposed study comprises two folds: (1) PSO-enabled objective function incorporating actual and predicted values of essential energy attributes, is implemented to reduce the trading cost, (2) an intelligent time-aware energy sharing strategy to determine the role of peers, and foster the harness of renewable energy to meet the energy requirements. The study also comprehensively analyzes essential nano-grid energy parameters and predicts energy load, consumption, and cost to grasp the time-interval-based energy trends. In addition, an optimal ESS charging and discharging operation is devised to manage excess power efficiently. The proposed model is validated on the case study containing data of 12 nanogrid houses. The outcomes yield that the proposed study holds significant potential in reducing the trading cost and optimally sharing the energy within the P2P network.
Sensors
With the increasing number of mobile devices and IoT devices across a wide range of real-life app... more With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the t...
Symmetry, 2021
The scientific community has presented various citation classification models to refute the conce... more The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine lear...
Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and... more Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing t...
Computers, Materials & Continua
Since the introduction of the Internet of Things (IoT), several researchers have been exploring i... more Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs' environment and when lower and higher SNRs information is carried by the different categories of MUs.
Sensors
High energy consumption, rising environmental concerns and depleting fossil fuels demand an incre... more High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by near...
Computers, Materials & Continua
Sleep stage classification can provide important information regarding neonatal brain development... more Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemblebased automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.
Sustainability
The demand for multimedia content over the Internet protocol network is growing exponentially wit... more The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out f...
Applied Sciences
Current developments in information technology and increased inclination towards smart cities hav... more Current developments in information technology and increased inclination towards smart cities have led to the initiation of a plethora of features by technology-oriented companies (i.e., car manufacturers) to improve users’ privacy and comfort. The invention of smart vehicle technology paved the way for the excessive use of machine-to-machine technologies. Moreover, third-party sharing of financial services are also introduced that support machine-to-machine (M2M) communication. These monetary systems’ prime focus is on improving reliability and security; however, they overlook aspects like behaviors and users’ need. For instance, people often hand over their bank cards or share their credentials with their colleagues to withdraw money on their behalf. Such behaviors may originate issues about privacy and security that can have severe losses for the card owner. This paper presents a novel blockchain-based strategy for payment of fueling of smart cars without any human interaction wh...
Sensors
This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization,... more This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization alg...
IEEE Access, 2022
The scientific community believes that peer-to-peer energy trading will dominate a significant po... more The scientific community believes that peer-to-peer energy trading will dominate a significant portion of forthcoming power generation systems research. Despite a plethora of optimal energy trading solutions, optimizing the trading cost and intelligent formation of energy sharing strategies are deemed exigent problems. Contemplating the excessive rise of energy demands across the globe, this study introduces a predictive optimization-based nanogrid energy trading model that minimizes energy trading cost and provides an optimal energy sharing plan between peers connected within a nanogrid network/cluster. The proposed study comprises two folds: (1) PSO-enabled objective function incorporating actual and predicted values of essential energy attributes, is implemented to reduce the trading cost, (2) an intelligent time-aware energy sharing strategy to determine the role of peers, and foster the harness of renewable energy to meet the energy requirements. The study also comprehensively analyzes essential nano-grid energy parameters and predicts energy load, consumption, and cost to grasp the time-interval-based energy trends. In addition, an optimal ESS charging and discharging operation is devised to manage excess power efficiently. The proposed model is validated on the case study containing data of 12 nanogrid houses. The outcomes yield that the proposed study holds significant potential in reducing the trading cost and optimally sharing the energy within the P2P network.
Sensors
With the increasing number of mobile devices and IoT devices across a wide range of real-life app... more With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the t...
Symmetry, 2021
The scientific community has presented various citation classification models to refute the conce... more The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine lear...
Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and... more Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing t...
Computers, Materials & Continua
Since the introduction of the Internet of Things (IoT), several researchers have been exploring i... more Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs' environment and when lower and higher SNRs information is carried by the different categories of MUs.
Sensors
High energy consumption, rising environmental concerns and depleting fossil fuels demand an incre... more High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by near...
Computers, Materials & Continua
Sleep stage classification can provide important information regarding neonatal brain development... more Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemblebased automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.
Sustainability
The demand for multimedia content over the Internet protocol network is growing exponentially wit... more The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out f...
Applied Sciences
Current developments in information technology and increased inclination towards smart cities hav... more Current developments in information technology and increased inclination towards smart cities have led to the initiation of a plethora of features by technology-oriented companies (i.e., car manufacturers) to improve users’ privacy and comfort. The invention of smart vehicle technology paved the way for the excessive use of machine-to-machine technologies. Moreover, third-party sharing of financial services are also introduced that support machine-to-machine (M2M) communication. These monetary systems’ prime focus is on improving reliability and security; however, they overlook aspects like behaviors and users’ need. For instance, people often hand over their bank cards or share their credentials with their colleagues to withdraw money on their behalf. Such behaviors may originate issues about privacy and security that can have severe losses for the card owner. This paper presents a novel blockchain-based strategy for payment of fueling of smart cars without any human interaction wh...
Sensors
This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization,... more This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization alg...