Energy Efficient Clustering Technique for VANET

Computers & Electrical Engineering, 2020

Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobilitybased 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.

Vehicular Ad-hoc Networks (VANET) have been significantly expanding to perform automotive services related to enhancing road networks, congestion flow, and even real-time traffic jams. Amid modern transportation systems, traffic quality has been relied on exchanging massive amounts of up-to-date information about their physical position and status with traffic management centers; this yields what’s called the ”data dissemination” problem. This type of phenomenon allows data propagation in a collaboratively and efficient manner to boost realtime performance. However, it brings several limitations in bandwidth usage and power consumption resulting in the premature end of network lifetime, as well as continuous break in path connection leading to lack of communication reliability and increasing packet loss probability. As a result, adjusting the rate of data dissemination over the network has become inescapable. The main objective of this study is to present a vehicle system design met...

2015

Clustering can be used in VANETs to partition the network into smaller groups of moving vehicles embedded with computing and network devices. Each cluster having a cluster head (CH), Gateway (GW) node and junction those are responsible for all management and coordination tasks of its cluster. We have designed algorithm for CH, GW Selection, Packet forwarding & Junction services. This paper presents the developed algorithm to provide Efficient and secure communication to the vehicles that allow drivers to warn other vehicles of potential dangers, and also provide valuable information to specified vehicle.

Electronics, 2020

In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission ...

2018

The Vehicular Ad-Hoc Network (VANET) paradigm offers the opportunity of extending Intelligent Transport System (ITS) by supporting its applications through vehicle-to-vehicle communications, notably in the areas where the infrastructure is inexistent, in failure, or overloaded. However, the complexity induced by ad hoc network management raises many challenges that have to be solved such as the sharing of bandwidth resources, the limitations on the duration of the connections between the vehicles, and the application-specific quality of service (QoS) requirements. Recently, the Chain-Branch-Leaf clustering scheme (CBL) has been proposed for vehicle-to-vehicle ad hoc routing that combines the information of road configuration, vehicle mobility, and link quality in order to build an efficient clustering connecting the entire VANET through a flexible backbone. This work presents a comparative study between the native Multipoint relaying clustering used in the Optimized Link State Routi...

In the past few years we are witnessing increased interest in the research of inter-vehicle communications. Due to vehicle specific movement patterns new algorithms and solutions have to be developed. Clustering is a technique for grouping nodes in geographical vicinity together, making the network more robust and scalable. This article presents an overview of proposed clustering algorithms for use in vehicular ad-hoc network (VANET). We survey different clustering algorithms and highlight their objectives, features, specialties and possible limitations. Varieties of different approaches have been observed whereby typically each one focuses on different performance metric. Diverse are also complexities of algorithms and the input data they use and relay on. With this article, readers can have a more thorough and delicate understanding of ad hoc clustering and the research trends in this area. The most promising solutions show the significance of reused concepts from the field of social network analysis. CH CN CN CN CN CN CN Fig. 1. Ideal cluster with cluster head in the center and cluster nodes around.

2017

Routing protocols for vehicular ad hoc networks resort to clustering to optimize network performance. In existing proposals, cluster-heads are chosen based on various metrics such as the number of its direct neighbors, the quality of the links, etc. Other clustering techniques consider the geographic environment of the roads, and they choose one cluster-head for each space subdivision. The clustering scheme proposed in this work combines the information on road configuration, vehicle mobility and link quality in order to build a structure similar to vehicular network infrastructure, while relying only on the vehicles. The evaluations show that this scheme allows creating and maintaining during a significant time a small number of stable connected groups, in most cases, just one in each traffic direction. This clustering scheme can be integrated into any reactive, proactive, or geographic ad hoc routing protocol in order to optimize the flooding and simplify route maintenance. And it...

International Journal of Innovative Technology and Exploring Engineering (IJITEE

The rising popularity of network based technologies has witnessed growing interests in research of inter-vehicular communications. In this scenario, Vehicular Ad-hoc Network (VANET) has evolved as the largely admired network traffic routing and control system. The main idea of this work is to enhance quality of clustering approach implemented in VANET. To achieve this, authors proposed the addition of two new elements in the existing clustering architecture, namely, the number of cluster heads required in a specific simulation area and the selection of paramount candidate to be used as cluster head. Supervised learning technique is implemented in the employed methodology. The proposed architecture is evaluated in terms of Jitter and Packet Delivery Ratio (PDR). The simulation results demonstrated that the node variation with PDR shows relatively higher average PDR for polynomial kernel as compared to the average PDR for linear kernel.

SUMMARY With an emergence of information and communication technologies, there is an increase in the demands of providing safety and comfort to the passengers during their mobility on the road. These demands can be fulfilled by one of the most popular networks of its type—vehicular adhoc networks (VANETs). As vehicles in VANETs are constrained with respect to the available resources such as computation and storage, lot of energy is consumed to perform a number of complex operations, which may lead to the emission of harmful CO 2 emission that effect the global warming system. Moreover, because of high velocity led by constant topological changes, it is a challenging task to maintain quality of service with respect to parameters such as high throughput, and minimum end-to-end delay. Hence, an intelligent approach is required to optimize the various complex operations in this environment, which may led to the minimum emission of CO 2 and other gasses. To address these issues, this paper proposes an efficient energy-aware predictive clustering scheme for vehicles. Efficient algorithms are designed for future mobility predictions and average variations of vehicles on the road. The algorithms estimate the clustering duration and total vehicles in the cluster. The performance of the designed algorithms is studied using extensive simulations by varying the number of vehicles and cluster durations in comparison with existing benchmarked scheme in the literature. The results obtained show that the proposed scheme is superior in comparison with the existing scheme of its category.

IEEE Access

Clustering is a promising technique to manage network resources efficiently and, in vehicular communications it is used to group vehicles with similar characteristics managed by a selected vehicle called a Cluster Head (CH). Due to the highly dynamic topology in vehicular networks, a CH selection process becomes a challenging task. Thus, this paper presents a new clustering scheme, namely, Efficient Cluster Head Selection (ECHS) scheme to select the most suitable CHs. The proposed ECHS scheme introduces important conditions pertaining to the methods deployed in constructing clusters before starting the CH selection. For instance, based on the ECHS rules the ideal CH is the one that centralizes the cluster. This is because it will remain connected as long as possible with its neighbors. The ECHS scheme also guarantees proper clustering distribution in the network, so that the distance between two consecutive clusters are adjusted carefully. Such conditions are guaranteed to effectively cluster vehicles in the road and make the ECHS scheme works better than its counterpart. Simulation experiments are conducted to examine the performance of the ECHS and the results demonstrate that the ECHS scheme achieves the design objectives in terms of CH lifetime, Cluster Member Lifetime (CML), Packet Loss Ratio(PLR), Overhead for Clustering(OC), Average Packet Delay (APD), and Cluster Number (CN). INDEX TERMS Clustering, cluster head, cluster gateway candidate, vehicular ad hoc networks.