Mohammad Mukhtaruzzaman

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 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...

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...

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.

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.

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 ...

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.