Papers by Mohammad Mukhtaruzzaman
Computer Networks, 2023
Scalability presents a significant challenge in vehicular communication, particularly when there ... more Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Most of the existing clustering protocols are optimized for highways only and some protocols are optimized for intersections only. The clusters that are created by the protocols which are optimized for highways breakdown at the intersections where clustering is needed more. On the other hand, the clusters that are created by the protocols optimized for intersections create extra clusters or break unnecessary on straight-roads which reduces cluster stability. Moreover, the lack of intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, and movement at the intersection, also contributes to cluster stability problems. A dynamic clustering protocol that efficiently combines all the mobility parameters can resolve these issues in VANETs by providing stability in both intersections and highways. To achieve high stability in VANET clustering, a Stable Dynamic feedback-based Predictive Clustering (SDPC) protocol is proposed for VANET to ensure cluster stability in both highway and intersection scenarios. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future position of the vehicles, relative distance between vehicles, movement of vehicles at the intersection, degree of vehicles, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering protocols in various road topologies, in terms of the average cluster head change rate, the average duration of cluster head, the average duration of cluster member, and the average clustering overhead. The simulation results show that SDPC outperforms the existing protocols, achieving higher clustering stability in terms of cluster head change rate (50%), cluster head duration (15%), cluster member duration (6%), and clustering overhead (35%).
Computer Networks, 2023
Scalability presents a significant challenge in vehicular communication, particularly when there ... more Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Most of the existing clustering protocols are optimized for highways only and some protocols are optimized for intersections only. The clusters that are created by the protocols which are optimized for highways breakdown at the intersections where clustering is needed more. On the other hand, the clusters that are created by the protocols optimized for intersections create extra clusters or break unnecessary on straight-roads which reduces cluster stability. Moreover, the lack of intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, and movement at the intersection, also contributes to cluster stability problems. A dynamic clustering protocol that efficiently combines all the mobility parameters can resolve these issues in VANETs by providing stability in both intersections and highways. To achieve high stability in VANET clustering, a Stable Dynamic feedback-based Predictive Clustering (SDPC) protocol is proposed for VANET to ensure cluster stability in both highway and intersection scenarios. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future position of the vehicles, relative distance between vehicles, movement of vehicles at the intersection, degree of vehicles, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering protocols in various road topologies, in terms of the average cluster head change rate, the average duration of cluster head, the average duration of cluster member, and the average clustering overhead. The simulation results show that SDPC outperforms the existing protocols, achieving higher clustering stability in terms of cluster head change rate (50%), cluster head duration (15%), cluster member duration (6%), and clustering overhead (35%).
Stable dynamic feedback-based predictive clustering protocol for vehicular Ad hoc networks
Computer Networks
Vehicular communication is an essential part of a smart city. Scalability is a major issue for ve... more Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication, specially, when the number of vehicles increases at any given point. Vehicles also suffer some other problems such as broadcast problem. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either straight road or for intersection. Moreover, the absence of the intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, movement at the intersection etc., results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of all the mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a novel robust and dynamic clustering algorithm stable dynamic predictive clustering (SDPC) for VANET is proposed in this paper. In contrast to previous studies, vehicle relative velocity, vehicle position, vehicle distance, transmission range, and vehicle density are considered in the creation of a cluster, whereas relative distance, movement at the intersection, degree of vehicles are considered to select the cluster head. From the mobility parameters the future road scenario is constructed. The cluster is created, and the cluster head is selected based on the future construction of the road. The performance of SDPC is compared in various road topologies with four existing VANET clustering algorithms in terms of the average cluster head change rate, the average cluster head duration, the average cluster member duration, and the ratio of clustering overhead in terms of total packet transmission. The simulation result shows SDPC outperforms the existing algorithms and achieved better clustering stability.
Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles joi... more Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation crite...
Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers ma... more Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers many problems such as scalability. To solve scalability and other problems of VANET, clustering is proposed. VANET clustering is different than any other kind of clustering due to the high mobility of the vehicles. Likewise, VANET and VANET clustering, VANET simulator requires some unique features such as internet based real-time data processing, huge data analysis, the complex calculation to maintain hierarchy among the vehicles, etc.; however, neither web based VANET simulator nor clustering module available in the existing simulators. Therefore, a simulator that will be able to simulate any feature of VANET equipped with a clustering module and accessible via the internet is a growing need in vehicular communication research. At the Telecom and Network Research Lab (TNRL), University of Oklahoma, we have developed a fully functional discrete-event VANET simulator that includes all the f...
ArXiv, 2021
Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers ma... more Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers many problems such as scalability. To solve scalability and other problems of VANET, clustering is proposed. VANET clustering is different than any other kind of clustering due to the high mobility of the vehicles. Likewise, VANET and VANET clustering, VANET simulator requires some unique features such as internet based real-time data processing, huge data analysis, the complex calculation to maintain hierarchy among the vehicles, etc.; however, neither web based VANET simulator nor clustering module available in the existing simulators. Therefore, a simulator that will be able to simulate any feature of VANET equipped with a clustering module and accessible via the internet is a growing need in vehicular communication research. At the Telecom and Network Research Lab (TNRL), University of Oklahoma, we have developed a fully functional discrete-event VANET simulator that includes all the f...
ArXiv, 2020
Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles joi... more Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation crite...
and Technology, has been accepted as satisfactory for the partial fulfillment of the requirements... more and Technology, has been accepted as satisfactory for the partial fulfillment of the requirements for the degree of Master of Science in Engineering (Computer Science and Engineering) and approved as to its style and contents. Examination held on May 21, 2011.
Annals of Telecommunications, 2021
Vehicular communication is an essential part of a smart city. Scalability is a major issue for ve... more Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either cluster head or cluster member duration. Moreover, the absence of the intelligent use of mobility parameters, such as direction, movement, position, and velocity, results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a new robust and dynamic mobility-based clustering algorithm junction-based clustering for VANET (JCV) is proposed in this paper. In contrast to previous studies, transmission range, moving direction of the vehicle at the next junction, and vehicle density are considered in the creation of a cluster, whereas relative position, movement at the junction, degree of a node, and time spent on the road are considered to select the cluster head. The performance of JCV is compared with two existing VANET clustering algorithms in terms of the average cluster head duration, the average cluster member duration, the average number of cluster head change, and the percentage of vehicles participating in the clustering process. The simulation result shows JCV outperforms the existing algorithms and achieved better stability.
Computers & Electrical Engineering, 2020
Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles joi... more Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms.
Journal of Network and Computer Applications, 2013
Abstract Network Mobility (NEMO) basic support protocol maintains the connectivity when Mobile Ro... more Abstract Network Mobility (NEMO) basic support protocol maintains the connectivity when Mobile Router (MR) of a mobile network changes its point of attachment to the Internet by establishing a bidirectional tunnel between the MR and the Home Agent (HA). A packet from a Correspondent Node (CN) traverses through the tunnel to reach the mobile network. Nesting occurs in NEMO when a MR's new attachment point is in another mobile network that has also moved away from its home link. The level of tunneling increases as the level ...
Drafts by Mohammad Mukhtaruzzaman
Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers ma... more Vehicular ad hoc network (VANET) is an integral part of vehicular communication. VANET suffers many problems such as scalability. To solve scalability and other problems of VANET, clustering is proposed. VANET clustering is different than any other kind of clustering due to the high mobility of the vehicles. Likewise, VANET and VANET clustering, VANET simulator requires some unique features such as internet based real-time data processing, huge data analysis, the complex calculation to maintain hierarchy among the vehicles, etc.; however, neither web based VANET simulator nor clustering module available in the existing simulators. Therefore, a simulator that will be able to simulate any feature of VANET equipped with a clustering module and accessible via the internet is a growing need in vehicular communication research. At the Telecom and Network Research Lab (TNRL), University of Oklahoma, we have developed a fully functional discrete-event VANET simulator that includes all the features of VANET clustering. Moreover, the cloud-based VANET simulator (CVANETSIM) is coming with an easy and interactive web interface. To our best of our knowledge, CVANETSIM is the first of its kind which integrates features of the VANET simulator, built-in VANET clustering module, and accessible through the internet.
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Papers by Mohammad Mukhtaruzzaman
Drafts by Mohammad Mukhtaruzzaman