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

PLoS ONE ◽  
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.

scholarly journals An Efficient Data Transmission in VANET Using Clustering Method

2017 ◽  
Vol 63 (3) ◽  
pp. 309-313 ◽  
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.

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

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880329 ◽  
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.

scholarly journals Location-Aware Distributed Clustering with Eliminating GPS in Vehicular Ad-hoc Networks

2020 ◽  
Author(s):  
Samane Beheshti ◽  
Sahar Adabi ◽  
Ali Rezaee
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Cluster Head ◽  
Traffic Load ◽  
Delivery Ratio ◽  
The Road ◽  
Location Aware ◽  
Hoc Networks

Vehicular ad-hoc network (VANET) is a type of mobile network which is used for establishing connection between vehicles (M2M) and also between vehicles and nearby stationary equipment which are often road-side equipment. The main target of VANET is to provide security and convenience for the passengers. In order to achieve this goal, a special electronic device called OBU (On-Board Unit) is embedded in each vehicle which makes the connection between vehicles and between the vehicles and the road-side equipment possible. In this paper, the Location-Aware Clustering in Vehicular Ad-hoc Networks (LAC-VANET) is proposed. We try to achieve the main and major goal in VANET networks, i.e. fast propagation of security and urgent messages in ITS systems, using clustering and selecting the best cluster head based on Fuzzy logic such that the cluster head can transfer important information such as the obstacles and accidents detected on the road with a suitable speed and without creating a large traffic load in the vehicle network in order to notify other vehicles and prevent the danger and vehicle accidents. Moreover, LAC-VANET method is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the VANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), end to end delay, and packet loss rate.

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