Mohammad Aldossary - Profile on Academia.edu (original) (raw)

Papers by Mohammad Aldossary

IEEE access, 2024

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, cri... more Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs' service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud). INDEX TERMS Unmanned aerial vehicle (UAV), cloud computing, fog computing, collaborative UAVs, energy-efficiency, task offloading.

IEEE access, 2024

Advancements in the agricultural sector are essential because the need for food is rising as the ... more Advancements in the agricultural sector are essential because the need for food is rising as the population of the world is expanding day by day. Traditional agricultural practices are not able to fulfill these needs. Furthermore, these practices are manual and are not optimized resulting in the wastage of resources, which is not suitable for resource-constrained agricultural environments. Besides this, the Internet of Things (IoT) network is playing an important role in the modern farming system. In this paper, we introduce an innovative IoT-enabled hybrid model for smart agriculture, integrating Machine Learning (ML) and Artificial Intelligence (AI) algorithms to provide a cost-effective and reliable decision-making system. Furthermore, we introduce a robust anomaly detection mechanism while applying the capabilities of Multilayer Perceptron (MLP), Naïve Bayes, and Support Vector Machine (SVM) on the dry beans' dataset. Hybrid models, combining neural networks with Random Forest and SVM, were also explored for anomaly detection in the dataset. Furthermore, deep learning models known as MobileNetV2, VGG16, and InceptionV3 are used for the classification of soil type datasets. The hybrid deep learning models were also developed, incorporating InceptionV3 with Long Short-Term Memory (LSTM) and VGG16 with fully connected dense layers. Two types of data sets are used in this study, which are the dry beans dataset (2021) and soil type Dataset (2024). Both datasets contain images. The ML techniques are applied to these datasets for anomaly detection. The simulations results show that the classification performance of the MobileNetV2 model, it has an accuracy and recall of 0.97. It shows that the model can correctly identify the soil type around 97%. On the other hand, the hybrid model combining random forest and neural network achieved an accuracy of 92%, further validating the effectiveness of our approach. Furthermore, the SVM model achieves an impressive overall accuracy of 0.93. Additionally, this accuracy is further enhanced with the integration of SVM and neural networks. Similarly, the hybrid model combining inception V3 with the LSTM layer exhibits a notable accuracy of 0.91, highlighting its efficiency in accurately classifying various instances. Lastly, the hybrid model employing random forest and neural network architecture achieves a commendable accuracy of 92%.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Computers, materials & continua/Computers, materials & continua (Print), 2024

I know the meaning of plagiarism, and I declare that, this dissertation is completely my personal... more I know the meaning of plagiarism, and I declare that, this dissertation is completely my personal work without any support from any external party. The dissertation is being submitted for the degree of Master of Science in Engineering at the University of Cape Town. The dissertation has not been submitted before for any degree or examination in any other university.

Research Square (Research Square), Mar 12, 2021

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased ... more Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

The journal of contemporary dental practice, Sep 28, 2021

Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and pan... more Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and panoramic radiographs (PRs) and its relation to tooth type, arch, facial sides, presence of occlusal interferences, and type of tooth movements. Materials and methods: A total of 65 patients with their SCs and PRs were recruited. Supraerupted tooth type, arches, sides involved, and the presence of occlusal interferences were recorded. The SCs for supraerupted teeth were photographed, and supraeruption from PRs was recorded from the patients' files. The values were transferred to a software program and assessed. Results: The highest frequency was observed among the younger-age group and molars in both arches. Working side (WS) and retruded cuspal position (RCP) interferences were recorded the highest. Supraeruption values of 0.7-1.2 mm accounted for 47.5% (38) of the total. Tilting and tipping of teeth were the highest, followed by buccolingual displacement. Kappa tests showed good intraexaminer reliability and Bland-Altman plot showed 95% confidence interval band. Conclusions: No significant differences were observed in the supraeruption values between the type of tooth among different subgroups of SCs and PRs. Significant differences were recorded between the types and sites of teeth, with a higher ratio observed in molar teeth, mandibular arch, and young age group. RCP and WS were the most recorded occlusal interferences and buccolingual displacement, and tilting and tipping were the most noticeable occlusal tooth movements. Clinical significance: Diagnosis and measurements of supraeruption are essential, useful, and significant steps before treatments for replacement of missing tooth/teeth as well as corrections of occlusal interferences during different mandibular movements.

Influence of Surface Type with Coffee Immersion on Surface Topography and Optical and Mechanical Properties of Selected Ceramic Materials

Medical Science Monitor

De-interleaving is a critical function in Electronic Warfare (EW) that has not received much atte... more De-interleaving is a critical function in Electronic Warfare (EW) that has not received much attention in the literature regarding on-line Electronic Intelligence (ELINT) application. In ELINT, online analysis is important in order to allow for e cient data collection and for support of operational decisions. This dissertation proposed a de-interleaving solution for use with ELINT/ElectronicSupport-Measures (ESM) receivers for purposes of ELINT with on-line application. The proposed solution does not require complex integration with existing EW systems or modi cations to their sub-systems. Before proposing the solution, on-line de-interleaving algorithms were surveyed. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be e ective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over other surveyed...

The Journal of Contemporary Dental Practice, 2021

Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and pan... more Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and panoramic radiographs (PRs) and its relation to tooth type, arch, facial sides, presence of occlusal interferences, and type of tooth movements. Materials and methods: A total of 65 patients with their SCs and PRs were recruited. Supraerupted tooth type, arches, sides involved, and the presence of occlusal interferences were recorded. The SCs for supraerupted teeth were photographed, and supraeruption from PRs was recorded from the patients' files. The values were transferred to a software program and assessed. Results: The highest frequency was observed among the younger-age group and molars in both arches. Working side (WS) and retruded cuspal position (RCP) interferences were recorded the highest. Supraeruption values of 0.7-1.2 mm accounted for 47.5% (38) of the total. Tilting and tipping of teeth were the highest, followed by buccolingual displacement. Kappa tests showed good intraexaminer reliability and Bland-Altman plot showed 95% confidence interval band. Conclusions: No significant differences were observed in the supraeruption values between the type of tooth among different subgroups of SCs and PRs. Significant differences were recorded between the types and sites of teeth, with a higher ratio observed in molar teeth, mandibular arch, and young age group. RCP and WS were the most recorded occlusal interferences and buccolingual displacement, and tilting and tipping were the most noticeable occlusal tooth movements. Clinical significance: Diagnosis and measurements of supraeruption are essential, useful, and significant steps before treatments for replacement of missing tooth/teeth as well as corrections of occlusal interferences during different mandibular movements.

Process development by parallel operation of paraffin isomerization unit with reformer

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Computer Systems Science and Engineering

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Computers, Materials & Continua

IEEE Access

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-to-end network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Sensors

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining p... more Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be ad...

Cornell University - arXiv, May 14, 2022

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-toend network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Computers, materials & continua, 2022

Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and... more Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060, NEOM city has been proposed to be the "Saudi hub" for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global electricity demand by 2030. Thus, reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions. Therefore, this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming (MILP) model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia. This approach considers the Saudi National Fiber Network (SNFN) as the backbone of Saudi Internet infrastructure. First, we compare two different scenarios of data center locations. The first scenario considers a traditional cloud data center located in Jeddah and Riyadh, whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure. Then, we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs. After that, we optimize the energy efficiency of different cloud data centers' locations (in the SNFN) to reduce the associated carbon emissions and energy costs. Simulation results show that the proposed approach can save up to 94% of the carbon emissions and 62% of the energy cost compared to the current cloud physical topology. These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources. Finally, we design a heuristic algorithm to verify the proposed approach, and it gives equivalent results to the MILP model.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

Computers, Materials & Continua, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the most cost-effective decision(s) to handle the service performance variation. The evaluation on a Cloud testbed shows that the proposed approach is capable of predicting the workload and power consumption as well as estimating the live migration and auto-scaling total cost for VMs during service operation, with a high prediction accuracy based on historical workload patterns.

Pricing mechanisms employed by di erent service providers significantly influence the role of clo... more Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

Computer Systems Science and Engineering, 2021

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used t... more In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.

Future Generation Computer Systems, 2018

This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in clou... more This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments.

IEEE access, 2024

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, cri... more Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs' service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud). INDEX TERMS Unmanned aerial vehicle (UAV), cloud computing, fog computing, collaborative UAVs, energy-efficiency, task offloading.

IEEE access, 2024

Advancements in the agricultural sector are essential because the need for food is rising as the ... more Advancements in the agricultural sector are essential because the need for food is rising as the population of the world is expanding day by day. Traditional agricultural practices are not able to fulfill these needs. Furthermore, these practices are manual and are not optimized resulting in the wastage of resources, which is not suitable for resource-constrained agricultural environments. Besides this, the Internet of Things (IoT) network is playing an important role in the modern farming system. In this paper, we introduce an innovative IoT-enabled hybrid model for smart agriculture, integrating Machine Learning (ML) and Artificial Intelligence (AI) algorithms to provide a cost-effective and reliable decision-making system. Furthermore, we introduce a robust anomaly detection mechanism while applying the capabilities of Multilayer Perceptron (MLP), Naïve Bayes, and Support Vector Machine (SVM) on the dry beans' dataset. Hybrid models, combining neural networks with Random Forest and SVM, were also explored for anomaly detection in the dataset. Furthermore, deep learning models known as MobileNetV2, VGG16, and InceptionV3 are used for the classification of soil type datasets. The hybrid deep learning models were also developed, incorporating InceptionV3 with Long Short-Term Memory (LSTM) and VGG16 with fully connected dense layers. Two types of data sets are used in this study, which are the dry beans dataset (2021) and soil type Dataset (2024). Both datasets contain images. The ML techniques are applied to these datasets for anomaly detection. The simulations results show that the classification performance of the MobileNetV2 model, it has an accuracy and recall of 0.97. It shows that the model can correctly identify the soil type around 97%. On the other hand, the hybrid model combining random forest and neural network achieved an accuracy of 92%, further validating the effectiveness of our approach. Furthermore, the SVM model achieves an impressive overall accuracy of 0.93. Additionally, this accuracy is further enhanced with the integration of SVM and neural networks. Similarly, the hybrid model combining inception V3 with the LSTM layer exhibits a notable accuracy of 0.91, highlighting its efficiency in accurately classifying various instances. Lastly, the hybrid model employing random forest and neural network architecture achieves a commendable accuracy of 92%.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Computers, materials & continua/Computers, materials & continua (Print), 2024

I know the meaning of plagiarism, and I declare that, this dissertation is completely my personal... more I know the meaning of plagiarism, and I declare that, this dissertation is completely my personal work without any support from any external party. The dissertation is being submitted for the degree of Master of Science in Engineering at the University of Cape Town. The dissertation has not been submitted before for any degree or examination in any other university.

Research Square (Research Square), Mar 12, 2021

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased ... more Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

The journal of contemporary dental practice, Sep 28, 2021

Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and pan... more Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and panoramic radiographs (PRs) and its relation to tooth type, arch, facial sides, presence of occlusal interferences, and type of tooth movements. Materials and methods: A total of 65 patients with their SCs and PRs were recruited. Supraerupted tooth type, arches, sides involved, and the presence of occlusal interferences were recorded. The SCs for supraerupted teeth were photographed, and supraeruption from PRs was recorded from the patients' files. The values were transferred to a software program and assessed. Results: The highest frequency was observed among the younger-age group and molars in both arches. Working side (WS) and retruded cuspal position (RCP) interferences were recorded the highest. Supraeruption values of 0.7-1.2 mm accounted for 47.5% (38) of the total. Tilting and tipping of teeth were the highest, followed by buccolingual displacement. Kappa tests showed good intraexaminer reliability and Bland-Altman plot showed 95% confidence interval band. Conclusions: No significant differences were observed in the supraeruption values between the type of tooth among different subgroups of SCs and PRs. Significant differences were recorded between the types and sites of teeth, with a higher ratio observed in molar teeth, mandibular arch, and young age group. RCP and WS were the most recorded occlusal interferences and buccolingual displacement, and tilting and tipping were the most noticeable occlusal tooth movements. Clinical significance: Diagnosis and measurements of supraeruption are essential, useful, and significant steps before treatments for replacement of missing tooth/teeth as well as corrections of occlusal interferences during different mandibular movements.

Influence of Surface Type with Coffee Immersion on Surface Topography and Optical and Mechanical Properties of Selected Ceramic Materials

Medical Science Monitor

De-interleaving is a critical function in Electronic Warfare (EW) that has not received much atte... more De-interleaving is a critical function in Electronic Warfare (EW) that has not received much attention in the literature regarding on-line Electronic Intelligence (ELINT) application. In ELINT, online analysis is important in order to allow for e cient data collection and for support of operational decisions. This dissertation proposed a de-interleaving solution for use with ELINT/ElectronicSupport-Measures (ESM) receivers for purposes of ELINT with on-line application. The proposed solution does not require complex integration with existing EW systems or modi cations to their sub-systems. Before proposing the solution, on-line de-interleaving algorithms were surveyed. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be e ective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over other surveyed...

The Journal of Contemporary Dental Practice, 2021

Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and pan... more Aims and objectives: This study compared the supraeruption of teeth for study casts (SCs) and panoramic radiographs (PRs) and its relation to tooth type, arch, facial sides, presence of occlusal interferences, and type of tooth movements. Materials and methods: A total of 65 patients with their SCs and PRs were recruited. Supraerupted tooth type, arches, sides involved, and the presence of occlusal interferences were recorded. The SCs for supraerupted teeth were photographed, and supraeruption from PRs was recorded from the patients' files. The values were transferred to a software program and assessed. Results: The highest frequency was observed among the younger-age group and molars in both arches. Working side (WS) and retruded cuspal position (RCP) interferences were recorded the highest. Supraeruption values of 0.7-1.2 mm accounted for 47.5% (38) of the total. Tilting and tipping of teeth were the highest, followed by buccolingual displacement. Kappa tests showed good intraexaminer reliability and Bland-Altman plot showed 95% confidence interval band. Conclusions: No significant differences were observed in the supraeruption values between the type of tooth among different subgroups of SCs and PRs. Significant differences were recorded between the types and sites of teeth, with a higher ratio observed in molar teeth, mandibular arch, and young age group. RCP and WS were the most recorded occlusal interferences and buccolingual displacement, and tilting and tipping were the most noticeable occlusal tooth movements. Clinical significance: Diagnosis and measurements of supraeruption are essential, useful, and significant steps before treatments for replacement of missing tooth/teeth as well as corrections of occlusal interferences during different mandibular movements.

Process development by parallel operation of paraffin isomerization unit with reformer

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Computer Systems Science and Engineering

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Computers, Materials & Continua

IEEE Access

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-to-end network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Sensors

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining p... more Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be ad...

Cornell University - arXiv, May 14, 2022

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-toend network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Computers, materials & continua, 2022

Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and... more Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060, NEOM city has been proposed to be the "Saudi hub" for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global electricity demand by 2030. Thus, reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions. Therefore, this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming (MILP) model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia. This approach considers the Saudi National Fiber Network (SNFN) as the backbone of Saudi Internet infrastructure. First, we compare two different scenarios of data center locations. The first scenario considers a traditional cloud data center located in Jeddah and Riyadh, whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure. Then, we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs. After that, we optimize the energy efficiency of different cloud data centers' locations (in the SNFN) to reduce the associated carbon emissions and energy costs. Simulation results show that the proposed approach can save up to 94% of the carbon emissions and 62% of the energy cost compared to the current cloud physical topology. These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources. Finally, we design a heuristic algorithm to verify the proposed approach, and it gives equivalent results to the MILP model.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

Computers, Materials & Continua, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the most cost-effective decision(s) to handle the service performance variation. The evaluation on a Cloud testbed shows that the proposed approach is capable of predicting the workload and power consumption as well as estimating the live migration and auto-scaling total cost for VMs during service operation, with a high prediction accuracy based on historical workload patterns.

Pricing mechanisms employed by di erent service providers significantly influence the role of clo... more Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

Computer Systems Science and Engineering, 2021

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used t... more In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.

Future Generation Computer Systems, 2018

This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in clou... more This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments.