Arshad Ali - Academia.edu (original) (raw)

Papers by Arshad Ali

Computers, Materials & Continua

Minimizing the energy consumption to increase the life span and performance of multiprocessor sys... more Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multicore system. High utilization factor (u i) on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on u i 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Computers, Materials & Continua

Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing powe... more Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%-15% because excessive on-chip temperature shortens the chip's life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor (u i) on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%-4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, 2011

We propose a new scheme for reliable transport, both for unicast and multicast flows, in Delay To... more We propose a new scheme for reliable transport, both for unicast and multicast flows, in Delay Tolerant Networks (DTNs). Reliability is ensured through the use of Global Selective ACKnowledgements (G-SACKs) which contain detailed (and potentially global) information about the receipt of packets at all the destinations. The motivation for using G-SACKs comes from the observation that one should take the maximum advantage of the contact opportunities which occur quite infrequently in DTNs. We also propose sharing of "packet header space" with G-SACK information and allow for random linear coding at the relay nodes. Our results from extensive simulations of the proposed scheme quantify the gains due to each new feature.

Routing in Opportunistic Networks, 2013

Sparse network with mostly isolated nodes Mostly disconnected-No contemporaneous end-to-end path ... more Sparse network with mostly isolated nodes Mostly disconnected-No contemporaneous end-to-end path exists at any time Nodes communicate when they come within the radio range Requires store-carry-and-forward method of routing

International Journal of Computer Science & Network Security, 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expec... more The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

The classification method used in this research work is one in which a common set of features are... more The classification method used in this research work is one in which a common set of features are used jointly in a single decision step. The classification analyses are chosen during experimental steps; planning the classification optimizes the data and enables conditions to be classified with the single run of the decision. This paper presents the classification of the terrain images; which are divided in to patches of same size and each patch is classified as either possessing features of interest or possesses no features of interest at all. As the classification method used is a single decision step method; in order to have a good classifier performance, a number of experiments are performed on several images with varying conditions; each experiment on an image is done and considered independently. Experimental results are presented using real images, the result clearly identify the regions with man-made features. This technique for terrain classification is developed for air ve...

Journal of Engineering and Applied Sciences, 2019

Wireless Sensor Networks (WSNs) are amongst the most important of the new emerging technologies a... more Wireless Sensor Networks (WSNs) are amongst the most important of the new emerging technologies and have shown an explosive growth in recent years for monitoring physical phenomena. Large scale WSNs face various challenges such as lack of coverage, large deployment areas and need of efficient sensor positioning. This paper introduces an approach for sensor management by using Kriging interpolation. The proposed technique affords monitoring of phenomena of interest in a distributed manner. A very good accuracy is achieved by using the available data coming from different sensor nodes. This is illustrated over an example for temperature monitoring.

Sensors, 2021

Recently, the concept of combining ‘things’ on the Internet to provide various services has gaine... more Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management...

Applied Sciences

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the ... more The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security ...

IEEE Access, 2020

Assigning accurate and timely priorities to bugs manually is resource consuming and effects addre... more Assigning accurate and timely priorities to bugs manually is resource consuming and effects addressing important bugs. In the existing work single feature is used which leads to information loss because bugs have a lot of features including ''severity'', ''component'', ''operation system'', ''owner'', ''status'', ''assigned to'', ''summary'' etc. In this research, the authors proposed an improved model based on problem title, severity, and component for bug prioritization. We converted these textual features to numeric features using Term Frequency Inverse Document Frequency. During conversion, 5591 new features are generated, which increase complexity and running time of algorithms. To minimize these aspects, non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) algorithms are used. Our proposed model is a combination of feature reduction, clustering, and classification algorithms. Clustering is performed on all and reduced features. For clustering X-Mean and K-Mean algorithms are used. SVM and Naive Bayes classifiers are applied on all features, reduced features, and on clustered features. For experiments chromium, eclipse, net beans, mozilla, and free desktop datasets are used. Experimental results reveal better performance of model, both with all features and with reduced features in terms of precision, recall, f-score, and accuracy. Maximum improvement is achieved with reduced features. With all features chromium, eclipse, free desktop, mozilla and net beans achieved 22.46%, 8.32%, 30.93%, 25.79% and 37.78% respectively improvement in accuracy. With reduced features chromium, elipse, free desktop, mozilla, net beans achieved 14.64%, 8.81%, 33.22%, 34.37% and 41.01% accuracy respectively. Overall classification with clustering and reduced features performed better than classification on all features, classification with clustering on all features, and classification on reduced features. In all the approaches SVM classifier outperformed Naive Bayes in terms of precision, recall, f-score, and accuracy. On average maximum accuracy is achieved by SVM with NMF and X-Mean clustering. INDEX TERMS Quality software, bugs, textual features.

International Journal of Environmental Research

IET Wireless Sensor Systems, 2016

Many wireless sensor network (WSN) applications rely on precise location or distance information.... more Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This paper presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that proposed algorithm delivers approximately 98% prediction accuracy.

The Internet of Things (IoT) has revolutionized innovation to collect and store the information r... more The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.

Assigning accurate and timely priorities to bugs manually is resource consuming and effects addre... more Assigning accurate and timely priorities to bugs manually is resource consuming and effects addressing important bugs. In the existing work single feature is used which leads to information loss because bugs have a lot of features including ''severity'', ''component'', ''operation system'', ''owner'', ''status'', ''assigned to'', ''summary'' etc. In this research, the authors proposed an improved model based on problem title, severity, and component for bug prioritization. We converted these textual features to numeric features using Term Frequency Inverse Document Frequency. During conversion, 5591 new features are generated, which increase complexity and running time of algorithms. To minimize these aspects, non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) algorithms are used. Our proposed model is a combination of feature reduction, clustering, and classification algorithms. Clustering is performed on all and reduced features. For clustering X-Mean and K-Mean algorithms are used. SVM and Naive Bayes classifiers are applied on all features, reduced features, and on clustered features. For experiments chromium, eclipse, net beans, mozilla, and free desktop datasets are used. Experimental results reveal better performance of model, both with all features and with reduced features in terms of precision, recall, f-score, and accuracy. Maximum improvement is achieved with reduced features. With all features chromium, eclipse, free desktop, mozilla and net beans achieved 22.46%, 8.32%, 30.93%, 25.79% and 37.78% respectively improvement in accuracy. With reduced features chromium, elipse, free desktop, mozilla, net beans achieved 14.64%, 8.81%, 33.22%, 34.37% and 41.01% accuracy respectively. Overall classification with clustering and reduced features performed better than classification on all features, classification with clustering on all features, and classification on reduced features. In all the approaches SVM classifier outperformed Naive Bayes in terms of precision, recall, f-score, and accuracy. On average maximum accuracy is achieved by SVM with NMF and X-Mean clustering. INDEX TERMS Quality software, bugs, textual features.

The monitoring of real time Blood Glucose level in diabetes patient is a big problem to be addres... more The monitoring of real time Blood Glucose level in diabetes patient is a big problem to be addressed in near future to treat patients efficiently. There are some kind of the

LabView is used to design the system to monitor and control lab experiments remotely over the Int... more LabView is used to design the system to monitor and control lab experiments remotely over the Internet. Web based embedded system is designed and presented in this paper along with required hardware to perform experiments remotely in allocated time slot. A unique system is designed to monitor and control room temperature remotely. To assess the performance of the designed system and the hardware, an experiment is designed to control room temperature remotely. The Temperature Control System (TCS) publish over the internet by using web server along with embedded live video of the equipment to control and change various required variables. LabView and the equipment from National Instruments is used to perform the experiment and the designed system can be controlled anywhere from the planet by using secure login detail only which exist in the database of server. As the experiments take long time to be completed and during this time the performer need to be presented in the Lab. By implementing this type of system, it will save the time of the performer and it can be utilized in other activities. Also, by designing and implementing this approach students from other Universities can also utilized the hardware and collaboration between institutions will be enhanced. The designed system presented in this article shows promising results which are presented in coming sections of this paper. The performed experiments are recorded and banked in the repository of the institution for future reference along with results.

Saudi Arabia has taken various initiatives, including the use of technology to improve teaching a... more Saudi Arabia has taken various initiatives, including the use of technology to improve teaching and learning in higher education. With 25 government and 27 private universities, it is fast becoming a popular study destination for international students from other countries. Islamic University of Madinah (IUM) which is one of the oldest and top universities in Saudi Arabia, introduced the EvalTools Learning Management System (LMS) in 2013 for enhanced teaching and learning environments. This research conducted a survey to study the perception of students about the impact of EvalTools LMS on teaching and learning. The survey was conducted in session 2017-18 through a five-point Likert scale questionnaire having 26 response items that were grouped into four constructs/variables: efficiency, performance, usage, and effectiveness. The results reveal that among the four constructs, efficiency has the highest mean score of 3.33, which implies that most students believe that EvalTools increases efficiency. While performance has a mean of 3.25, usage has a mean of 3.23, and effectiveness has a mean of 3.30, suggesting that students have a positive perception that EvalTools is easy to use and is effective in achieving the course outcomes. Most of the students agree that performance is increased by the use of EvalTools. The findings of this study conclude that the majority of students have a positive perception that EvalTools LMS increases the efficiency, effectiveness, and performance of the users.

Minimizing the energy consumption to increase the life span and performance of multiprocessor sys... more Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multicore system. High utilization factor (u i) on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on u i 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

There are many Learning Management Systems (LMS) tools like Blackboard, WebCT, EvalTool, Moodle e... more There are many Learning Management Systems (LMS) tools like Blackboard, WebCT, EvalTool, Moodle etc.to support teaching and learning that have produced remarkable results for teachers, students and higher education institutions. There is a remarkable increase in the use of these LMS tools by universities around the globe. Islamic University of Medina (IUM), Saudi Arabia uses two LMS, namely Blackboard and EvalTools, as an online instructional environment. This research was conducted in session 2019-20 to examine the perception of teachers about the effect of EvalTools LMS on teaching and learning in IUM. A five-point Likert scale survey questionnaire that consisted of 30 items was developed to measure the perception of teachers through relevant variables such as efficiency, effectiveness, usage and performance. The findings reveal that most teachers have a positive perception that EvalTools LMS increases the efficiency, effectiveness and performance of the users, while a slight majority of teachers have a negative perception about the ease of usage of this tool.

Computers, Materials & Continua

Minimizing the energy consumption to increase the life span and performance of multiprocessor sys... more Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multicore system. High utilization factor (u i) on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on u i 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Computers, Materials & Continua

Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing powe... more Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%-15% because excessive on-chip temperature shortens the chip's life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor (u i) on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%-4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, 2011

We propose a new scheme for reliable transport, both for unicast and multicast flows, in Delay To... more We propose a new scheme for reliable transport, both for unicast and multicast flows, in Delay Tolerant Networks (DTNs). Reliability is ensured through the use of Global Selective ACKnowledgements (G-SACKs) which contain detailed (and potentially global) information about the receipt of packets at all the destinations. The motivation for using G-SACKs comes from the observation that one should take the maximum advantage of the contact opportunities which occur quite infrequently in DTNs. We also propose sharing of "packet header space" with G-SACK information and allow for random linear coding at the relay nodes. Our results from extensive simulations of the proposed scheme quantify the gains due to each new feature.

Routing in Opportunistic Networks, 2013

Sparse network with mostly isolated nodes Mostly disconnected-No contemporaneous end-to-end path ... more Sparse network with mostly isolated nodes Mostly disconnected-No contemporaneous end-to-end path exists at any time Nodes communicate when they come within the radio range Requires store-carry-and-forward method of routing

International Journal of Computer Science & Network Security, 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expec... more The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

The classification method used in this research work is one in which a common set of features are... more The classification method used in this research work is one in which a common set of features are used jointly in a single decision step. The classification analyses are chosen during experimental steps; planning the classification optimizes the data and enables conditions to be classified with the single run of the decision. This paper presents the classification of the terrain images; which are divided in to patches of same size and each patch is classified as either possessing features of interest or possesses no features of interest at all. As the classification method used is a single decision step method; in order to have a good classifier performance, a number of experiments are performed on several images with varying conditions; each experiment on an image is done and considered independently. Experimental results are presented using real images, the result clearly identify the regions with man-made features. This technique for terrain classification is developed for air ve...

Journal of Engineering and Applied Sciences, 2019

Wireless Sensor Networks (WSNs) are amongst the most important of the new emerging technologies a... more Wireless Sensor Networks (WSNs) are amongst the most important of the new emerging technologies and have shown an explosive growth in recent years for monitoring physical phenomena. Large scale WSNs face various challenges such as lack of coverage, large deployment areas and need of efficient sensor positioning. This paper introduces an approach for sensor management by using Kriging interpolation. The proposed technique affords monitoring of phenomena of interest in a distributed manner. A very good accuracy is achieved by using the available data coming from different sensor nodes. This is illustrated over an example for temperature monitoring.

Sensors, 2021

Recently, the concept of combining ‘things’ on the Internet to provide various services has gaine... more Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management...

Applied Sciences

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the ... more The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security ...

IEEE Access, 2020

Assigning accurate and timely priorities to bugs manually is resource consuming and effects addre... more Assigning accurate and timely priorities to bugs manually is resource consuming and effects addressing important bugs. In the existing work single feature is used which leads to information loss because bugs have a lot of features including ''severity'', ''component'', ''operation system'', ''owner'', ''status'', ''assigned to'', ''summary'' etc. In this research, the authors proposed an improved model based on problem title, severity, and component for bug prioritization. We converted these textual features to numeric features using Term Frequency Inverse Document Frequency. During conversion, 5591 new features are generated, which increase complexity and running time of algorithms. To minimize these aspects, non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) algorithms are used. Our proposed model is a combination of feature reduction, clustering, and classification algorithms. Clustering is performed on all and reduced features. For clustering X-Mean and K-Mean algorithms are used. SVM and Naive Bayes classifiers are applied on all features, reduced features, and on clustered features. For experiments chromium, eclipse, net beans, mozilla, and free desktop datasets are used. Experimental results reveal better performance of model, both with all features and with reduced features in terms of precision, recall, f-score, and accuracy. Maximum improvement is achieved with reduced features. With all features chromium, eclipse, free desktop, mozilla and net beans achieved 22.46%, 8.32%, 30.93%, 25.79% and 37.78% respectively improvement in accuracy. With reduced features chromium, elipse, free desktop, mozilla, net beans achieved 14.64%, 8.81%, 33.22%, 34.37% and 41.01% accuracy respectively. Overall classification with clustering and reduced features performed better than classification on all features, classification with clustering on all features, and classification on reduced features. In all the approaches SVM classifier outperformed Naive Bayes in terms of precision, recall, f-score, and accuracy. On average maximum accuracy is achieved by SVM with NMF and X-Mean clustering. INDEX TERMS Quality software, bugs, textual features.

International Journal of Environmental Research

IET Wireless Sensor Systems, 2016

Many wireless sensor network (WSN) applications rely on precise location or distance information.... more Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This paper presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that proposed algorithm delivers approximately 98% prediction accuracy.

The Internet of Things (IoT) has revolutionized innovation to collect and store the information r... more The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.

Assigning accurate and timely priorities to bugs manually is resource consuming and effects addre... more Assigning accurate and timely priorities to bugs manually is resource consuming and effects addressing important bugs. In the existing work single feature is used which leads to information loss because bugs have a lot of features including ''severity'', ''component'', ''operation system'', ''owner'', ''status'', ''assigned to'', ''summary'' etc. In this research, the authors proposed an improved model based on problem title, severity, and component for bug prioritization. We converted these textual features to numeric features using Term Frequency Inverse Document Frequency. During conversion, 5591 new features are generated, which increase complexity and running time of algorithms. To minimize these aspects, non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) algorithms are used. Our proposed model is a combination of feature reduction, clustering, and classification algorithms. Clustering is performed on all and reduced features. For clustering X-Mean and K-Mean algorithms are used. SVM and Naive Bayes classifiers are applied on all features, reduced features, and on clustered features. For experiments chromium, eclipse, net beans, mozilla, and free desktop datasets are used. Experimental results reveal better performance of model, both with all features and with reduced features in terms of precision, recall, f-score, and accuracy. Maximum improvement is achieved with reduced features. With all features chromium, eclipse, free desktop, mozilla and net beans achieved 22.46%, 8.32%, 30.93%, 25.79% and 37.78% respectively improvement in accuracy. With reduced features chromium, elipse, free desktop, mozilla, net beans achieved 14.64%, 8.81%, 33.22%, 34.37% and 41.01% accuracy respectively. Overall classification with clustering and reduced features performed better than classification on all features, classification with clustering on all features, and classification on reduced features. In all the approaches SVM classifier outperformed Naive Bayes in terms of precision, recall, f-score, and accuracy. On average maximum accuracy is achieved by SVM with NMF and X-Mean clustering. INDEX TERMS Quality software, bugs, textual features.

The monitoring of real time Blood Glucose level in diabetes patient is a big problem to be addres... more The monitoring of real time Blood Glucose level in diabetes patient is a big problem to be addressed in near future to treat patients efficiently. There are some kind of the

LabView is used to design the system to monitor and control lab experiments remotely over the Int... more LabView is used to design the system to monitor and control lab experiments remotely over the Internet. Web based embedded system is designed and presented in this paper along with required hardware to perform experiments remotely in allocated time slot. A unique system is designed to monitor and control room temperature remotely. To assess the performance of the designed system and the hardware, an experiment is designed to control room temperature remotely. The Temperature Control System (TCS) publish over the internet by using web server along with embedded live video of the equipment to control and change various required variables. LabView and the equipment from National Instruments is used to perform the experiment and the designed system can be controlled anywhere from the planet by using secure login detail only which exist in the database of server. As the experiments take long time to be completed and during this time the performer need to be presented in the Lab. By implementing this type of system, it will save the time of the performer and it can be utilized in other activities. Also, by designing and implementing this approach students from other Universities can also utilized the hardware and collaboration between institutions will be enhanced. The designed system presented in this article shows promising results which are presented in coming sections of this paper. The performed experiments are recorded and banked in the repository of the institution for future reference along with results.

Saudi Arabia has taken various initiatives, including the use of technology to improve teaching a... more Saudi Arabia has taken various initiatives, including the use of technology to improve teaching and learning in higher education. With 25 government and 27 private universities, it is fast becoming a popular study destination for international students from other countries. Islamic University of Madinah (IUM) which is one of the oldest and top universities in Saudi Arabia, introduced the EvalTools Learning Management System (LMS) in 2013 for enhanced teaching and learning environments. This research conducted a survey to study the perception of students about the impact of EvalTools LMS on teaching and learning. The survey was conducted in session 2017-18 through a five-point Likert scale questionnaire having 26 response items that were grouped into four constructs/variables: efficiency, performance, usage, and effectiveness. The results reveal that among the four constructs, efficiency has the highest mean score of 3.33, which implies that most students believe that EvalTools increases efficiency. While performance has a mean of 3.25, usage has a mean of 3.23, and effectiveness has a mean of 3.30, suggesting that students have a positive perception that EvalTools is easy to use and is effective in achieving the course outcomes. Most of the students agree that performance is increased by the use of EvalTools. The findings of this study conclude that the majority of students have a positive perception that EvalTools LMS increases the efficiency, effectiveness, and performance of the users.

Minimizing the energy consumption to increase the life span and performance of multiprocessor sys... more Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multicore system. High utilization factor (u i) on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on u i 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

There are many Learning Management Systems (LMS) tools like Blackboard, WebCT, EvalTool, Moodle e... more There are many Learning Management Systems (LMS) tools like Blackboard, WebCT, EvalTool, Moodle etc.to support teaching and learning that have produced remarkable results for teachers, students and higher education institutions. There is a remarkable increase in the use of these LMS tools by universities around the globe. Islamic University of Medina (IUM), Saudi Arabia uses two LMS, namely Blackboard and EvalTools, as an online instructional environment. This research was conducted in session 2019-20 to examine the perception of teachers about the effect of EvalTools LMS on teaching and learning in IUM. A five-point Likert scale survey questionnaire that consisted of 30 items was developed to measure the perception of teachers through relevant variables such as efficiency, effectiveness, usage and performance. The findings reveal that most teachers have a positive perception that EvalTools LMS increases the efficiency, effectiveness and performance of the users, while a slight majority of teachers have a negative perception about the ease of usage of this tool.