Cluster head selection algorithm in vehicular Ad Hoc networks

In mobile ad hoc network (MANET) cluster head selection is considered a gigantic challenge. In wireless sensor network LEACH protocol can be used to select cluster head on the bases of energy, but it is still a dispute in mobil ad hoc networks and especially when nodes are itinerant. In this paper we proposed an efficient cluster head selection algorithm (ECHSA), for selection of the cluster head efficiently in Mobile ad hoc networks. We evaluate our proposed algorithm through simulation in OMNet++ as well as on test bed; we experience the result according to our assumption. For further evaluation we also compare our proposed protocol with several other protocols like LEACH-C and consequences show perfection.

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An Efficient Data Transmission in VANET Using Clustering Method

International Journal of Electronics and Telecommunications ◽

10.1515/eletel-2017-0045 ◽

2017 ◽

Vol 63 (3) ◽

pp. 309-313 ◽

Cited By ~ 2

Author(s):

C. Suganthi Evangeline ◽

S. Appu

Keyword(s):

Ad Hoc Networks ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Cluster Head ◽

Data Gathering ◽

Link Quality ◽

Clustering Methods ◽

The Road ◽

Hoc Networks

Abstract A special type of Mobile Ad-hoc Networks (MANETs) which has frequent changes of topology and higher mobility is known as Vehicular Ad-hoc Networks (VANETs). In order to divide the network into groups of mobile vehicles and improve routing, data gathering, clustering is applied in VANETs. A stable clustering scheme based on adaptive multiple metric combining both the features of static and dynamic clustering methods is proposed in this work. Based on a new multiple metric method, a cluster head is selected among the cluster members which is taken from the mobility metrics such as position and time to leave the road segment, relative speed and Quality of Service metrics which includes neighborhood degree, link quality of the RSU and bandwidth. A higher QoS and cluster stability are achieved through the adaptive multiple metric. The results are simulated using NS2 and shows that this technique provides more stable cluster structured with the other methods.

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ClusterRep: A cluster-based reputation framework for balancing privacy and trust in vehicular participatory sensing

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718803299 ◽

2018 ◽

Vol 14 (9) ◽

pp. 155014771880329 ◽

Cited By ~ 6

Author(s):

Jin Wang ◽

Youyuan Wang ◽

Xiang Gu ◽

Liang Chen ◽

Jie Wan

Keyword(s):

Ad Hoc Networks ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Cluster Head ◽

Quality Data ◽

Participatory Sensing ◽

Sensitive Information ◽

User Privacy ◽

Building Trust ◽

Hoc Networks

In vehicular participatory sensing, vehicles may provide false data or low-quality data. Building trust in vehicular ad hoc networks is an efficient way to deal with this issue. On one hand, vehicles need to disclose necessary information to demonstrate their trustworthiness. On the other hand, vehicles tend to hide their sensitive information to preserve user privacy. Therefore, privacy and trust are conflict in vehicular ad hoc networks. A cluster-based reputation framework named ClusterRep is proposed to balance privacy and trust in vehicular ad hoc networks. In this framework, the cluster head collaborates with cluster members to change pseudonyms and reputation values. The experiments show the scalability and the effectiveness of the ClusterRep compared with Beta strategy and IncogniSense-floor strategy.

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Secure and Fair Cluster Head Selection Protocol for Enhancing Security in Mobile Ad Hoc Networks

The Scientific World JOURNAL ◽

10.1155/2014/608984 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 5

Author(s):

B. Paramasivan ◽

M. Kaliappan

Keyword(s):

Ad Hoc Networks ◽

Mobile Ad Hoc Networks ◽

Ad Hoc ◽

Cluster Head ◽

Secure Routing ◽

Network Simulator ◽

Cluster Head Selection ◽

Selection Protocol ◽

Mobile Ad Hoc ◽

Hoc Networks

Mobile ad hoc networks (MANETs) are wireless networks consisting of number of autonomous mobile devices temporarily interconnected into a network by wireless media. MANETs become one of the most prevalent areas of research in the recent years. Resource limitations, energy efficiency, scalability, and security are the great challenging issues in MANETs. Due to its deployment nature, MANETs are more vulnerable to malicious attack. The secure routing protocols perform very basic security related functions which are not sufficient to protect the network. In this paper, a secure and fair cluster head selection protocol (SFCP) is proposed which integrates security factors into the clustering approach for achieving attacker identification and classification. Byzantine agreement based cooperative technique is used for attacker identification and classification to make the network more attack resistant. SFCP used to solve this issue by making the nodes that are totally surrounded by malicious neighbors adjust dynamically their belief and disbelief thresholds. The proposed protocol selects the secure and energy efficient cluster head which acts as a local detector without imposing overhead to the clustering performance. SFCP is simulated in network simulator 2 and compared with two protocols including AODV and CBRP.

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An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

PLoS ONE ◽

10.1371/journal.pone.0250271 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250271

Author(s):

Ghassan Husnain ◽

Shahzad Anwar

Keyword(s):

Ad Hoc Networks ◽

Optimization Algorithm ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Cluster Head ◽

Grid Size ◽

Whale Optimization Algorithm ◽

Whale Optimization ◽

Cluster Optimization ◽

Hoc Networks

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

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