Badiea Al-Shaibani | University of Hail (original) (raw)
Papers by Badiea Al-Shaibani
2010 Second International Conference on Network Applications, Protocols and Services, 2010
Page 1. Modified Fast-Integrated Light-NEMOv6 Handoff in IEEE 802.16e BWA Networks 1Badiea Abdulk... more Page 1. Modified Fast-Integrated Light-NEMOv6 Handoff in IEEE 802.16e BWA Networks 1Badiea Abdulkarem Mohammed and 2Tat-Chee Wan 1School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang ...
Engineering, Technology & Applied Science Research, 2021
A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network ... more A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network without any pre-existing infrastructure. Multi-path routing protocols in MANETs try to discover and use multiple routes between source and destination nodes. Multipath routing is typically used to reduce average delay, increase transmission reliability, provide load balancing among multiple routes, and improve security and overall QoS (Quality of Service). In this paper, the Cluster-Based Routing Protocol (CBRP), which is a single path MANET protocol is enhanced to use multiple paths. The traffic will be distributed among multiple paths to reduce network traffic congestion and decrease delay. An analytical model is used for multipath and single path CBRP routing protocols in MANETs to estimate the end-to-end delay and queue length. The analytical results show that the average delay and average queue length in multipath CBRP are less than the average delay and queue length in single path ...
Eectronics, 2021
Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between n... more Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
Computers,Materials & Continua, 2022
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaem... more Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.
IJCSNS International Journal of Computer Science and Network Security, 2022
Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to th... more Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.
Sustainability, 2021
In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping nece... more In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.
Applied Sciences, 2021
Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the d... more Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.
Applied Science, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IJCSNS International Journal of Computer Science and Network Security, 2021
Contact between Vehicle-to-vehicle and vehicle-to-infrastructural is becoming increasingly popula... more Contact between Vehicle-to-vehicle and vehicle-to-infrastructural is becoming increasingly popular in recent years due to their crucial role in the field of intelligent transportation. Vehicular Ad-hoc networks (VANETs) security and privacy are of the highest value since a transparent wireless communication tool allows an intruder to intercept, tamper, reply and erase messages in plain text. The security of a VANET based intelligent transport system may therefore be compromised. There is a strong likelihood. Securing and maintaining message exchange in VANETs is currently the focal point of several security testing teams, as it is reflected in the number of authentication schemes. However, these systems have not fulfilled all aspects of security and privacy criteria. This study is an attempt to provide a detailed history of VANETs and their components; different kinds of attacks and all protection and privacy criteria for VANETs. This paper contributed to the existing literature by systematically analyzes and compares existing authentication and confidentiality systems based on all security needs, the cost of information and communication as well as the level of resistance to different types of attacks. This paper may be used as a guide and reference for any new VANET protection and privacy technologies in the design and development.
Applied Science, 2021
Waste Management System (WMS) is applied in smart cities and is supported by the Internet of Thin... more Waste Management System (WMS) is applied in smart cities and is supported by the Internet of Things (IoT). WMS involves several responsibilities, such as collection, disposal, and utilization of waste in relevant facilities. Efficient waste management has a considerable impact on the quality of life of citizens in smart cities. The interaction between wireless sensor networks and mobile ad-hoc networks (MANETs) with IoT provides excellent mobility for users, and reduces the deployment costs of networks. Data transfer via the intermediate connected devices must be maintained to shorten the distance between the devices. Intelligent routing algorithm is proposed to find efficient paths that comply with the WMS requirement constraints and avoid invalid paths that cause increased computation time. Chromosome intersection operation on genetic-based routing algorithm, intersection activation function, and intersection node table are proposed to avoid the similarity and redundancy of generated paths and keep the validity of the paths. This study improves the path finding, obtains good results, and increases the rate of efficient paths. Results show that the selected path is efficient in terms of distance, number of hops and number of iterations by using the proposed method. In addition, this proposed method outperforms DSR, which offers alternative paths with more efficiency.
Individuals who move between countries often struggle to acclimate to a new culture. Additionally... more Individuals who move between countries often struggle to acclimate to a new culture. Additionally, overseas students endure additional academic responsibilities as they acclimatize to a new country. Over 2,000 Saudi Arabian students are enrolled at Malaysian universities at the moment. Nonetheless, Saudi students in Malaysia face a number of challenges. Saudi Arabian postgraduate students’ psychological adjustment in Malaysia was explored in the present research, with language and cultural barriers playing a significant role. A total of 400 Saudi Arabian postgraduate students in Malaysia completed an online survey that they created and administered. Notably, the researchers discovered that students' language and cultural difficulties had no significant effect on their psychological adjustment.
Individuals who live in different countries face issues adjusting to a new culture. Furthermore, ... more Individuals who live in different countries face issues adjusting to a new culture. Furthermore, additional academic demands are confronted by overseas students while adjusting to a new nation. More than 2,000 Saudi Arabian students are currently enrolled in Malaysian universities. Nevertheless, there are several issues that Saudi students in Malaysia are facing. In the current study, Saudi Arabian postgraduate students' psychological adjustment in Malaysia was studied, in which self-esteem and social support were important contributed factors. A total of 400 Saudi Arabian postgraduate students in Malaysia participated in an online survey that they designed and completed themselves. Most significantly, the researchers found that both students' self-esteem and social support had a significant impact on their psychological adjustment.
NEMO basic support protocol was created by IETF NEMO working group to extend basic end-host mobil... more NEMO basic support protocol was created by IETF NEMO working group to extend basic end-host mobility support in, Mobile IP (MIP) protocol to provide network mobility support. However, the handover latency in this scheme is high, and not suitable for multimedia and real time applications. Many techniques have been proposed to solve this problem by parallelizing the dual layers handover and assigning the network layer handover part to the serving Mobile Router (PAR) or to the Home Agent (HA). These techniques either not fully parallelized or its advantages will be lost if the distance between the Mobile Router and its HA is so long. In this paper, we propose a new technique called Rapid-Integrated NEMO Handover (RINEMO) that keep its advantage whatever the distance between the MR and its HA and present a better parallelizing between the link layer and the network layer handover steps. Analytical results comparing the techniques are provided, showing that our technique have the lowest handover latency and lowest disruption time.
Wireless Sensor Network (WSN) management has several NP-hard optimization problems with multiobje... more Wireless Sensor Network (WSN) management has several NP-hard optimization problems with multiobjective space and variable solution space nature. Such problems are considered challenging and require a carefully developed and well-understood algorithm in behavior and performance. This article presents an open-source benchmarking framework that enables comparison within supporting multiobjective optimization metrics and networking measures. As the first of its type, the framework is designated as Variable-length Multiobjective Optimization (VLMOO). It includes three sets of mathematical benchmarking problems, WSN deployment problem with multiobjective, and variable-length nature. It is extensible and flexible in enabling interfacing with other algorithms with rich visualization graphs generation for the metrics.
Phishing attacks are security attacks that do not affect only individuals' or organizations' webs... more Phishing attacks are security attacks that do not affect only individuals' or organizations' websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network ... more A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network without any pre-existing infrastructure. Multi-path routing protocols in MANETs try to discover and use multiple routes between source and destination nodes. Multipath routing is typically used to reduce average delay, increase transmission reliability, provide load balancing among multiple routes, and improve security and overall QoS (Quality of Service). In this paper, the Cluster Based Routing Protocol (CBRP), which is a single path MANET protocol is enhanced to use multiple paths. The traffic will be distributed among multiple paths to reduce network traffic congestion and decrease delay. An analytical model is used for multipath and single path CBRP routing protocols in MANETs to estimate the end-to-end delay and queue length. The analytical results show that the average delay and average queue length in multipath CBRP are less than the average delay and queue length in single path CBRP.
In order to fulfil the demand for on-the-move and uninterrupted internet connectivity in Mobile N... more In order to fulfil the demand for on-the-move and uninterrupted internet connectivity in Mobile Networks, the IETF (Internet Engineering Task Force) has standardized NEMO Basic Support Protocol in as RFC 3963. However, providing a low latency handoff performance in mobile networks by using fast handover mechanism to ensure a seamless mobility for users is one of the main objectives of ongoing research activity. In this paper, we introduce Modified Fast-Integrated Light-NEMO Handoff scheme and proposed a final combination of the Modified Fast Integrated-Handover scheme with the Light-NEMO network model in order to provide a seamless mobility in nested mobile networks. Analytical results comparing the various schemes are presented, showing that MFINEMO able to decrease the handover latency and enhance the service disruption time during the handover operation.
NEMO was created to extend the basic end-host mobility support in the Mobile IP (MIP) protocol to... more NEMO was created to extend the basic end-host mobility support in the Mobile IP (MIP) protocol to provide network mobility support. However, the handover latency in NEMO is high and the nested tunnels' problem in the nested NEMO networks is not considered. Many schemes have been proposed to solve these problems depend on the Fast Mobile IP (FMIP) by optimizing its signaling procedure. Better optimized signaling procedure is proposed in this paper and using of a proposed Routing Optimization scheme as a solution for the lack of the nested tunnels' problem is proposed too. Analytical results comparing the proposed scheme with the others are provided, showing that our scheme has the lowest handover latency and disruption time.
2010 Second International Conference on Network Applications, Protocols and Services, 2010
Page 1. Modified Fast-Integrated Light-NEMOv6 Handoff in IEEE 802.16e BWA Networks 1Badiea Abdulk... more Page 1. Modified Fast-Integrated Light-NEMOv6 Handoff in IEEE 802.16e BWA Networks 1Badiea Abdulkarem Mohammed and 2Tat-Chee Wan 1School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang ...
Engineering, Technology & Applied Science Research, 2021
A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network ... more A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network without any pre-existing infrastructure. Multi-path routing protocols in MANETs try to discover and use multiple routes between source and destination nodes. Multipath routing is typically used to reduce average delay, increase transmission reliability, provide load balancing among multiple routes, and improve security and overall QoS (Quality of Service). In this paper, the Cluster-Based Routing Protocol (CBRP), which is a single path MANET protocol is enhanced to use multiple paths. The traffic will be distributed among multiple paths to reduce network traffic congestion and decrease delay. An analytical model is used for multipath and single path CBRP routing protocols in MANETs to estimate the end-to-end delay and queue length. The analytical results show that the average delay and average queue length in multipath CBRP are less than the average delay and queue length in single path ...
Eectronics, 2021
Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between n... more Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
Computers,Materials & Continua, 2022
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaem... more Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.
IJCSNS International Journal of Computer Science and Network Security, 2022
Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to th... more Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.
Sustainability, 2021
In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping nece... more In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.
Applied Sciences, 2021
Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the d... more Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.
Applied Science, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IJCSNS International Journal of Computer Science and Network Security, 2021
Contact between Vehicle-to-vehicle and vehicle-to-infrastructural is becoming increasingly popula... more Contact between Vehicle-to-vehicle and vehicle-to-infrastructural is becoming increasingly popular in recent years due to their crucial role in the field of intelligent transportation. Vehicular Ad-hoc networks (VANETs) security and privacy are of the highest value since a transparent wireless communication tool allows an intruder to intercept, tamper, reply and erase messages in plain text. The security of a VANET based intelligent transport system may therefore be compromised. There is a strong likelihood. Securing and maintaining message exchange in VANETs is currently the focal point of several security testing teams, as it is reflected in the number of authentication schemes. However, these systems have not fulfilled all aspects of security and privacy criteria. This study is an attempt to provide a detailed history of VANETs and their components; different kinds of attacks and all protection and privacy criteria for VANETs. This paper contributed to the existing literature by systematically analyzes and compares existing authentication and confidentiality systems based on all security needs, the cost of information and communication as well as the level of resistance to different types of attacks. This paper may be used as a guide and reference for any new VANET protection and privacy technologies in the design and development.
Applied Science, 2021
Waste Management System (WMS) is applied in smart cities and is supported by the Internet of Thin... more Waste Management System (WMS) is applied in smart cities and is supported by the Internet of Things (IoT). WMS involves several responsibilities, such as collection, disposal, and utilization of waste in relevant facilities. Efficient waste management has a considerable impact on the quality of life of citizens in smart cities. The interaction between wireless sensor networks and mobile ad-hoc networks (MANETs) with IoT provides excellent mobility for users, and reduces the deployment costs of networks. Data transfer via the intermediate connected devices must be maintained to shorten the distance between the devices. Intelligent routing algorithm is proposed to find efficient paths that comply with the WMS requirement constraints and avoid invalid paths that cause increased computation time. Chromosome intersection operation on genetic-based routing algorithm, intersection activation function, and intersection node table are proposed to avoid the similarity and redundancy of generated paths and keep the validity of the paths. This study improves the path finding, obtains good results, and increases the rate of efficient paths. Results show that the selected path is efficient in terms of distance, number of hops and number of iterations by using the proposed method. In addition, this proposed method outperforms DSR, which offers alternative paths with more efficiency.
Individuals who move between countries often struggle to acclimate to a new culture. Additionally... more Individuals who move between countries often struggle to acclimate to a new culture. Additionally, overseas students endure additional academic responsibilities as they acclimatize to a new country. Over 2,000 Saudi Arabian students are enrolled at Malaysian universities at the moment. Nonetheless, Saudi students in Malaysia face a number of challenges. Saudi Arabian postgraduate students’ psychological adjustment in Malaysia was explored in the present research, with language and cultural barriers playing a significant role. A total of 400 Saudi Arabian postgraduate students in Malaysia completed an online survey that they created and administered. Notably, the researchers discovered that students' language and cultural difficulties had no significant effect on their psychological adjustment.
Individuals who live in different countries face issues adjusting to a new culture. Furthermore, ... more Individuals who live in different countries face issues adjusting to a new culture. Furthermore, additional academic demands are confronted by overseas students while adjusting to a new nation. More than 2,000 Saudi Arabian students are currently enrolled in Malaysian universities. Nevertheless, there are several issues that Saudi students in Malaysia are facing. In the current study, Saudi Arabian postgraduate students' psychological adjustment in Malaysia was studied, in which self-esteem and social support were important contributed factors. A total of 400 Saudi Arabian postgraduate students in Malaysia participated in an online survey that they designed and completed themselves. Most significantly, the researchers found that both students' self-esteem and social support had a significant impact on their psychological adjustment.
NEMO basic support protocol was created by IETF NEMO working group to extend basic end-host mobil... more NEMO basic support protocol was created by IETF NEMO working group to extend basic end-host mobility support in, Mobile IP (MIP) protocol to provide network mobility support. However, the handover latency in this scheme is high, and not suitable for multimedia and real time applications. Many techniques have been proposed to solve this problem by parallelizing the dual layers handover and assigning the network layer handover part to the serving Mobile Router (PAR) or to the Home Agent (HA). These techniques either not fully parallelized or its advantages will be lost if the distance between the Mobile Router and its HA is so long. In this paper, we propose a new technique called Rapid-Integrated NEMO Handover (RINEMO) that keep its advantage whatever the distance between the MR and its HA and present a better parallelizing between the link layer and the network layer handover steps. Analytical results comparing the techniques are provided, showing that our technique have the lowest handover latency and lowest disruption time.
Wireless Sensor Network (WSN) management has several NP-hard optimization problems with multiobje... more Wireless Sensor Network (WSN) management has several NP-hard optimization problems with multiobjective space and variable solution space nature. Such problems are considered challenging and require a carefully developed and well-understood algorithm in behavior and performance. This article presents an open-source benchmarking framework that enables comparison within supporting multiobjective optimization metrics and networking measures. As the first of its type, the framework is designated as Variable-length Multiobjective Optimization (VLMOO). It includes three sets of mathematical benchmarking problems, WSN deployment problem with multiobjective, and variable-length nature. It is extensible and flexible in enabling interfacing with other algorithms with rich visualization graphs generation for the metrics.
Phishing attacks are security attacks that do not affect only individuals' or organizations' webs... more Phishing attacks are security attacks that do not affect only individuals' or organizations' websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network ... more A MANET (Mobile Ad-hoc Network) is a group of mobile network nodes dynamically forming a network without any pre-existing infrastructure. Multi-path routing protocols in MANETs try to discover and use multiple routes between source and destination nodes. Multipath routing is typically used to reduce average delay, increase transmission reliability, provide load balancing among multiple routes, and improve security and overall QoS (Quality of Service). In this paper, the Cluster Based Routing Protocol (CBRP), which is a single path MANET protocol is enhanced to use multiple paths. The traffic will be distributed among multiple paths to reduce network traffic congestion and decrease delay. An analytical model is used for multipath and single path CBRP routing protocols in MANETs to estimate the end-to-end delay and queue length. The analytical results show that the average delay and average queue length in multipath CBRP are less than the average delay and queue length in single path CBRP.
In order to fulfil the demand for on-the-move and uninterrupted internet connectivity in Mobile N... more In order to fulfil the demand for on-the-move and uninterrupted internet connectivity in Mobile Networks, the IETF (Internet Engineering Task Force) has standardized NEMO Basic Support Protocol in as RFC 3963. However, providing a low latency handoff performance in mobile networks by using fast handover mechanism to ensure a seamless mobility for users is one of the main objectives of ongoing research activity. In this paper, we introduce Modified Fast-Integrated Light-NEMO Handoff scheme and proposed a final combination of the Modified Fast Integrated-Handover scheme with the Light-NEMO network model in order to provide a seamless mobility in nested mobile networks. Analytical results comparing the various schemes are presented, showing that MFINEMO able to decrease the handover latency and enhance the service disruption time during the handover operation.
NEMO was created to extend the basic end-host mobility support in the Mobile IP (MIP) protocol to... more NEMO was created to extend the basic end-host mobility support in the Mobile IP (MIP) protocol to provide network mobility support. However, the handover latency in NEMO is high and the nested tunnels' problem in the nested NEMO networks is not considered. Many schemes have been proposed to solve these problems depend on the Fast Mobile IP (FMIP) by optimizing its signaling procedure. Better optimized signaling procedure is proposed in this paper and using of a proposed Routing Optimization scheme as a solution for the lack of the nested tunnels' problem is proposed too. Analytical results comparing the proposed scheme with the others are provided, showing that our scheme has the lowest handover latency and disruption time.