IEEE Intelligent Transportation Systems Magazine, 2021
Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations ... more Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed.
This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression ch... more This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited ...
This study examines temporal gene expression (GE) patterns in a murine model of SARS-CoV infectio... more This study examines temporal gene expression (GE) patterns in a murine model of SARS-CoV infection. We focused on a Temporal Gene Set (TGS) comprising pro-inflammatory genes (TNF, NFKB1, VEGF-A) and VEGF-B. A systematic search of the NCBI Geo database for MA15 (SARS-CoV) pulmonary studies using C57BL Wild (WT) mice and filtering according to TGS GE patterns eluded seven datasets for further analysis. Encompassing the GE profiles from these datasets alluded to a rising and falling pattern in TNF and NFKB1 GE. Also, our findings reveal a temporal decrease in VEGF-A GE coinciding with an increase in VEGF-B GE post-immunogenic stimulation. Notably, differential responses were observed with the MA15 dosage and in comparison, to other antigens (dORF6 and NSP16). Further, the human SARS-CoV-2 gene enrichment in this murine study confirms the MA15 murine model’s relevance for SARS research. Our study also suggests potential interactions between SARS-CoV-2 Spike protein and VEGF-related rece...
Sepsis remains a major global health issue in pediatric and adult populations, largely due to a l... more Sepsis remains a major global health issue in pediatric and adult populations, largely due to a lack of understanding of its complex pathophysiology. Despite its high mortality rate, there have been few advancements in sepsis-specific therapies over recent decades. The study aimed to investigate the potential benefits of a genome-wide transcriptomic approach to sepsis in pediatric and adult populations in reducing sepsis-related mortality and enhancing sepsis guidelines. The scoping review explored gene expression data pertinent to developing sepsis guidelines related to its definition, classification, disease severity, molecular biomarking, and benchmarking. A system-biology approach using transcriptomics was adopted to enhance the understanding of sepsis at the mRNA gene expression level. The study involved a search of the PubMed database for original research or systematic reviews that involved transcriptomic application in the context of clinical sepsis published over a ten-year...
In recent years, educational institutions have worked hard to automate their work using more tren... more In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different ...
Background and AimAn essential issue for Kawasaki Disease (KD) is the development of coronary art... more Background and AimAn essential issue for Kawasaki Disease (KD) is the development of coronary artery disease. We decided to investigate (VEGF) subtype gene expression in KD due to the proangiogenic nature of Vasoactive Endothelial Growth Factor A (VEGF). VEGF-A is a known angiogenic molecule with pro-inflammatory effects, whereas the role of VEGFB has been less defined.MethodKD Microarray and RNA-seq datasets were selected using a comprehensive search strategy of the NCBI GEO Dataset, which resulted in eight studies from whole blood. This included three extensive studies in KD (KD1-KD3). Further, one study dataset from coronary artery tissue, the Coronary Artery Dataset (CAD), was also included to appreciate end-stage KD.ResultsIn CAD, cases of KD versus controls, VEGF-B was up-regulated (p = 4.932e-02). KD1, KD Acute versus convalescent samples, VEGF-A is up-regulated (p=1.258e-07) and VEGF-B was down-regulated (p=1.42e-28). Similar up regulation of VEGF-A and down regulation of VE...
2017 IEEE International Conference on Communications (ICC)
Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) par... more Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is originally designed for disseminating code updates through a wireless sensor network. Thus, when it comes to routing maintenance in LLNs, Trickle suffers from some issues related to power, convergence time, network overhead and load-distribution. In this paper, a new algorithm for maintaining the network topology in LLNs is developed motivated by Trickle weaknesses, namely, Drizzle algorithm. Unlike Trickle, Drizzle uses an adaptive suppression mechanism that permits the nodes to have different transmission probabilities consistent with their transmission history. Another distinctive feature of Drizzle in comparison with Trickle, is the absence of the listen-only period from Drizzle's intervals, thus, leading to faster convergence time. Furthermore, a new policy for setting the redundancy coefficient has been used to mitigate the negative effect of the short-listen problem presented when removing the listen-only period and to further boost the fairness in the network. Our extensive simulation experiments confirm the superiority of the proposed algorithm over Trickle under different operating conditions.
ICC 2021 - IEEE International Conference on Communications, 2021
Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large sca... more Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large scale environments due to its long-scale connection, energy efficiency, and cost-effectiveness. LoRa networks provide multiple configurable transmission parameters that greatly affect the performance of the overall network. To the best of our knowledge, the optimal combination of these parameters that can allow orthogonal simultaneous transmissions to be successfully decoded by the gateway has not been reported in the literature. Exploiting all the transmission parameters in the physical layer to find optimal combinations between them will inevitably increase the throughput of LoRa without affecting the energy consumption. Hence, in this paper, we propose annulus-based distribution algorithm of LoRa transmission parameters to mitigate wellknown and challenging issues, namely the capture effect and the limited scalability. The performance of the proposed algorithm has been compared with the Adaptive Data Rate(ADR) algorithm of LoRaWAN and the simulation results show that the proposed algorithm significantly outperforms the ADR especially in largescale dense networks. Specifically, the proposed algorithm has improved the network throughput by an average of 59% compared to the legacy LoRaWAN.
2017 International Symposium on Networks, Computers and Communications (ISNCC), 2017
Although energy efficiency in Wireless Sensor Networks (WSNs) has been investigated widely, it is... more Although energy efficiency in Wireless Sensor Networks (WSNs) has been investigated widely, it is still a challenging dilemma. Clustering mechanisms are among the most widely recommended approaches to sustaining a network throughout its lifetime. Despite a number of research activities associated with clustering in WSNs, some aspects of clustering have not yet been adequately investigated. The election of Cluster Head (CH) nodes is the key element in most clustering algorithms. The majority of the elected CHs in previous algorithms use a single metric as their principal criteria, such as remaining energy, distance to the base station (BS), and random probability. Unlike existing solutions, this paper proposes a new weighting based approach to electing CHs, considering the node's context; i.e. its transmission range, its degree, remaining energy and centrality from its neighbours. The novelty of our algorithm relates to the selection of a set of nodes with the highest weight as CH candidates, in order to compete for final CH. This will guarantee the selection of optimal cluster heads among sensor nodes. Moreover, this novel approach eliminates the re-clustering process for the entire network in each round, by rotating the CHs inside the created clusters in the first set up phase. The simulation experiments demonstrate that the new approach outperforms its counterparts (HEED, LCP and EEUC) with respect to the network's lifetime.
Initialization is the first and a major step in the implementation of evolutionary algorithms (EA... more Initialization is the first and a major step in the implementation of evolutionary algorithms (EAs). Although there are many common general methods to initialize EAs such as the pseudo-random number generator (PRNG), there is no single method that can fit every problem. This study provides a new, flexible, diversity-aware, and easy-to-implement initialization method for a genetic algorithm for the shortest path problem. The proposed algorithm, called stratified opposition-based sampling (SOBS), considers phenotype and genotype diversity while striving to achieve the best fitness for the initialization population. SOBS does not depend on a specific type of sampling, because the main goal is to stratify the sampling space. SOBS aims at an initial population with higher fitness and diversity in the phenotype and genotype. To investigate the performance of SOBS, four network models were used to simulate real-world networks. Compared with the most frequently used initialization method, that is, PRNG, SOBS provides more accurate solutions, better running time with less memory usage, and an initial population with higher fitness. Statistical analysis showed that SOBS yields solutions with higher accuracy in 68%-100% of the time. Although this study was focused on the genetic algorithm, it can
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018
The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized a... more The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource-constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load-balancing primitive. In this study, we show how RPL suffers from a loadbalancing problem that may harm both the reliability of the protocol and its network lifetime. To address this problem, a novel load-balancing scheme is introduced that significantly enhances the reliability of RPL and fosters the protocol's efficiency in terms of power consumption.
This document proposes an extended Objective Function(OF)that balances the number of child nodes ... more This document proposes an extended Objective Function(OF)that balances the number of child nodes of the parent nodes to avoid the overloading problem and ensure node lifetime maximization in the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). The standard OFs are used to build a Destination Oriented Directed Acyclic Graph (DODAG) where the bottleneck nodes may suffer from unbalanced traffic load. As a result, a part of the network may be disconnected as the energy of the overloaded preferred parent node will drain much faster than other candidate parents. Thus, a new RPL metric has been introduced to balance the traffic load over the network. Finally, the potential extra overhead has been mitigated using a new utilization technique.
The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized a... more The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource- constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load- balancing primitive. To address this problem, a novel load- balancing scheme is introduced that ensures a fair distribution of traffic among nodes while minimizing overhead
Vehicular ad hoc networks (VANETs) have been instrumental in intelligent transportation systems t... more Vehicular ad hoc networks (VANETs) have been instrumental in intelligent transportation systems that enhances road safety and road management significantly. This technology enables communication among vehicles where drivers can share road information conditions. However, users can threaten spectrum access caused by launching passive and active attacks that prevent nodes to access spectrum efficiently. Securing spectrum access has become critical issue in VANET to ensure reliability and trustworthiness. In this paper, a novel collaborative approach during the spectrum access process is proposed. In our approach, vehicles are divided into clusters and Road Side Unit (RSU) is used to manage spectrum access for each cluster. RSU monitors the traffic for each node and identifies the malicious and misbehaving nodes. The proposed approach measures the node’s data reliability using a decision function. The scheme is applicable to a wide range of VANET applications, such as traffic safety, c...
IEEE Intelligent Transportation Systems Magazine, 2021
Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations ... more Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed.
This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression ch... more This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited ...
This study examines temporal gene expression (GE) patterns in a murine model of SARS-CoV infectio... more This study examines temporal gene expression (GE) patterns in a murine model of SARS-CoV infection. We focused on a Temporal Gene Set (TGS) comprising pro-inflammatory genes (TNF, NFKB1, VEGF-A) and VEGF-B. A systematic search of the NCBI Geo database for MA15 (SARS-CoV) pulmonary studies using C57BL Wild (WT) mice and filtering according to TGS GE patterns eluded seven datasets for further analysis. Encompassing the GE profiles from these datasets alluded to a rising and falling pattern in TNF and NFKB1 GE. Also, our findings reveal a temporal decrease in VEGF-A GE coinciding with an increase in VEGF-B GE post-immunogenic stimulation. Notably, differential responses were observed with the MA15 dosage and in comparison, to other antigens (dORF6 and NSP16). Further, the human SARS-CoV-2 gene enrichment in this murine study confirms the MA15 murine model’s relevance for SARS research. Our study also suggests potential interactions between SARS-CoV-2 Spike protein and VEGF-related rece...
Sepsis remains a major global health issue in pediatric and adult populations, largely due to a l... more Sepsis remains a major global health issue in pediatric and adult populations, largely due to a lack of understanding of its complex pathophysiology. Despite its high mortality rate, there have been few advancements in sepsis-specific therapies over recent decades. The study aimed to investigate the potential benefits of a genome-wide transcriptomic approach to sepsis in pediatric and adult populations in reducing sepsis-related mortality and enhancing sepsis guidelines. The scoping review explored gene expression data pertinent to developing sepsis guidelines related to its definition, classification, disease severity, molecular biomarking, and benchmarking. A system-biology approach using transcriptomics was adopted to enhance the understanding of sepsis at the mRNA gene expression level. The study involved a search of the PubMed database for original research or systematic reviews that involved transcriptomic application in the context of clinical sepsis published over a ten-year...
In recent years, educational institutions have worked hard to automate their work using more tren... more In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different ...
Background and AimAn essential issue for Kawasaki Disease (KD) is the development of coronary art... more Background and AimAn essential issue for Kawasaki Disease (KD) is the development of coronary artery disease. We decided to investigate (VEGF) subtype gene expression in KD due to the proangiogenic nature of Vasoactive Endothelial Growth Factor A (VEGF). VEGF-A is a known angiogenic molecule with pro-inflammatory effects, whereas the role of VEGFB has been less defined.MethodKD Microarray and RNA-seq datasets were selected using a comprehensive search strategy of the NCBI GEO Dataset, which resulted in eight studies from whole blood. This included three extensive studies in KD (KD1-KD3). Further, one study dataset from coronary artery tissue, the Coronary Artery Dataset (CAD), was also included to appreciate end-stage KD.ResultsIn CAD, cases of KD versus controls, VEGF-B was up-regulated (p = 4.932e-02). KD1, KD Acute versus convalescent samples, VEGF-A is up-regulated (p=1.258e-07) and VEGF-B was down-regulated (p=1.42e-28). Similar up regulation of VEGF-A and down regulation of VE...
2017 IEEE International Conference on Communications (ICC)
Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) par... more Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is originally designed for disseminating code updates through a wireless sensor network. Thus, when it comes to routing maintenance in LLNs, Trickle suffers from some issues related to power, convergence time, network overhead and load-distribution. In this paper, a new algorithm for maintaining the network topology in LLNs is developed motivated by Trickle weaknesses, namely, Drizzle algorithm. Unlike Trickle, Drizzle uses an adaptive suppression mechanism that permits the nodes to have different transmission probabilities consistent with their transmission history. Another distinctive feature of Drizzle in comparison with Trickle, is the absence of the listen-only period from Drizzle's intervals, thus, leading to faster convergence time. Furthermore, a new policy for setting the redundancy coefficient has been used to mitigate the negative effect of the short-listen problem presented when removing the listen-only period and to further boost the fairness in the network. Our extensive simulation experiments confirm the superiority of the proposed algorithm over Trickle under different operating conditions.
ICC 2021 - IEEE International Conference on Communications, 2021
Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large sca... more Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large scale environments due to its long-scale connection, energy efficiency, and cost-effectiveness. LoRa networks provide multiple configurable transmission parameters that greatly affect the performance of the overall network. To the best of our knowledge, the optimal combination of these parameters that can allow orthogonal simultaneous transmissions to be successfully decoded by the gateway has not been reported in the literature. Exploiting all the transmission parameters in the physical layer to find optimal combinations between them will inevitably increase the throughput of LoRa without affecting the energy consumption. Hence, in this paper, we propose annulus-based distribution algorithm of LoRa transmission parameters to mitigate wellknown and challenging issues, namely the capture effect and the limited scalability. The performance of the proposed algorithm has been compared with the Adaptive Data Rate(ADR) algorithm of LoRaWAN and the simulation results show that the proposed algorithm significantly outperforms the ADR especially in largescale dense networks. Specifically, the proposed algorithm has improved the network throughput by an average of 59% compared to the legacy LoRaWAN.
2017 International Symposium on Networks, Computers and Communications (ISNCC), 2017
Although energy efficiency in Wireless Sensor Networks (WSNs) has been investigated widely, it is... more Although energy efficiency in Wireless Sensor Networks (WSNs) has been investigated widely, it is still a challenging dilemma. Clustering mechanisms are among the most widely recommended approaches to sustaining a network throughout its lifetime. Despite a number of research activities associated with clustering in WSNs, some aspects of clustering have not yet been adequately investigated. The election of Cluster Head (CH) nodes is the key element in most clustering algorithms. The majority of the elected CHs in previous algorithms use a single metric as their principal criteria, such as remaining energy, distance to the base station (BS), and random probability. Unlike existing solutions, this paper proposes a new weighting based approach to electing CHs, considering the node's context; i.e. its transmission range, its degree, remaining energy and centrality from its neighbours. The novelty of our algorithm relates to the selection of a set of nodes with the highest weight as CH candidates, in order to compete for final CH. This will guarantee the selection of optimal cluster heads among sensor nodes. Moreover, this novel approach eliminates the re-clustering process for the entire network in each round, by rotating the CHs inside the created clusters in the first set up phase. The simulation experiments demonstrate that the new approach outperforms its counterparts (HEED, LCP and EEUC) with respect to the network's lifetime.
Initialization is the first and a major step in the implementation of evolutionary algorithms (EA... more Initialization is the first and a major step in the implementation of evolutionary algorithms (EAs). Although there are many common general methods to initialize EAs such as the pseudo-random number generator (PRNG), there is no single method that can fit every problem. This study provides a new, flexible, diversity-aware, and easy-to-implement initialization method for a genetic algorithm for the shortest path problem. The proposed algorithm, called stratified opposition-based sampling (SOBS), considers phenotype and genotype diversity while striving to achieve the best fitness for the initialization population. SOBS does not depend on a specific type of sampling, because the main goal is to stratify the sampling space. SOBS aims at an initial population with higher fitness and diversity in the phenotype and genotype. To investigate the performance of SOBS, four network models were used to simulate real-world networks. Compared with the most frequently used initialization method, that is, PRNG, SOBS provides more accurate solutions, better running time with less memory usage, and an initial population with higher fitness. Statistical analysis showed that SOBS yields solutions with higher accuracy in 68%-100% of the time. Although this study was focused on the genetic algorithm, it can
2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018
The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized a... more The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource-constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load-balancing primitive. In this study, we show how RPL suffers from a loadbalancing problem that may harm both the reliability of the protocol and its network lifetime. To address this problem, a novel load-balancing scheme is introduced that significantly enhances the reliability of RPL and fosters the protocol's efficiency in terms of power consumption.
This document proposes an extended Objective Function(OF)that balances the number of child nodes ... more This document proposes an extended Objective Function(OF)that balances the number of child nodes of the parent nodes to avoid the overloading problem and ensure node lifetime maximization in the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). The standard OFs are used to build a Destination Oriented Directed Acyclic Graph (DODAG) where the bottleneck nodes may suffer from unbalanced traffic load. As a result, a part of the network may be disconnected as the energy of the overloaded preferred parent node will drain much faster than other candidate parents. Thus, a new RPL metric has been introduced to balance the traffic load over the network. Finally, the potential extra overhead has been mitigated using a new utilization technique.
The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized a... more The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource- constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load- balancing primitive. To address this problem, a novel load- balancing scheme is introduced that ensures a fair distribution of traffic among nodes while minimizing overhead
Vehicular ad hoc networks (VANETs) have been instrumental in intelligent transportation systems t... more Vehicular ad hoc networks (VANETs) have been instrumental in intelligent transportation systems that enhances road safety and road management significantly. This technology enables communication among vehicles where drivers can share road information conditions. However, users can threaten spectrum access caused by launching passive and active attacks that prevent nodes to access spectrum efficiently. Securing spectrum access has become critical issue in VANET to ensure reliability and trustworthiness. In this paper, a novel collaborative approach during the spectrum access process is proposed. In our approach, vehicles are divided into clusters and Road Side Unit (RSU) is used to manage spectrum access for each cluster. RSU monitors the traffic for each node and identifies the malicious and misbehaving nodes. The proposed approach measures the node’s data reliability using a decision function. The scheme is applicable to a wide range of VANET applications, such as traffic safety, c...
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Papers by Ahmed Al-dubai