Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications

Rivoirard, Lucas; Wahl, Martine; Sondi, Patrick; Berbineau, Marion; Gruyer, Dominique

doi:10.1016/j.adhoc.2017.10.007

Cited by 32 publications

(23 citation statements)

References 7 publications

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“…Moreover, CBL supports unicast communications and oriented broadcast (upstream or downstream) in the road traffic, which can be useful for some applications such as cooperative perception and cooperative localization [19]. Through simulation, we already showed that CBL reduces routing traffic load [18] in OLSR in comparison with the MPR, without degrading network performance for the applications, notably on end-toend delay and packet delivery ratio [20].…”

Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 98%

“…In a recent work [18], we presented in detail and discussed all these references. We also introduced a clustering scheme, chain-branch-leaf (CBL), that builds a virtual backbone in the VANET, similar to that obtained with RSUs, while relying only on V2V communications.…”

Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 99%

“…CBL is a completely distributed algorithm described in detail in [18]: each communication node initiates its own process. CBL creates a hierarchy between the nodes in order to build one-hop clusters so that each node of a cluster can directly communicate to the cluster-head without going through another relaying node.…”

Section: A Definitionsmentioning

confidence: 99%

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Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Rivoirard

Wahl

Sondi

2020

IEEE Trans. Intell. Transport. Syst.

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Routing protocols for vehicular ad hoc networks resort to clustering in order to optimize network performance. Concerning the Optimized Link State Routing (OLSR) protocol and the plethora of its derivatives, the multipoint relaying (MPR) technique has proven its efficiency as an accurate clustering scheme over the last two decades. However, it has been emphasized recently that the MPR technique, which was originally designed for open areas, does not benefit from the particular configuration of road sections which are intrinsically spatially constrained. A clustering scheme exploiting this particularity, namely Chain-Branch-Leaf (CBL), has been introduced in order to enhance the flooding of broadcast traffic, including that related to routing operations. In this paper, both MPR and CBL are evaluated through MATLAB simulation over several scenarios based on realistic road configurations and traffic generated with SUMO simulator. The results show that CBL actually reduces the number of nodes acting as relays (cluster-heads) in the network, thus decreasing the routing traffic related to creation and retransmission of topology control (TC) messages. Also, they show that, with CBL, the nodes chosen as relays remain longer in this role, thus favoring the overall network stability, and that most of the nodes remain attached longer to the same relay than with the MPR technique.

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Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 98%

Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 99%

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Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Rivoirard

Wahl

Sondi

2020

IEEE Trans. Intell. Transport. Syst.

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“…The designed algorithm failed to minimize the end-to-end delay for improving the routing strategy. A Chain-Branch-Leaf (CBL) clustering approach was presented in [12] for improving vehicle communication. But the reliable data delivery was not achieved since it failed to select the optimal route path between source to destination.An Artificial Bee Colony (ABC) algorithm was developed in [13] to select the best route with minimum cost for transmitting the data packets.…”

Section: Related Workmentioning

confidence: 99%

“…In (12), denotes a distance, represents the arrival time of beacon message from the cluster. denotes a transmitting time of the beacon message from the source cluster head.…”

Section: B Dolphin Swarm Optimization Based Routing In Vanetmentioning

confidence: 99%

Margin Boost Clustering based Multivariate Dolphin Swarm Optimization for Routing and Reliable Data Dissemination in VANET

Radhika¹,

Bhuvaneswari²

2019

IJITEE

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VANET is the Vehicular Ad hoc NETworks where the vehicle nodes communicate to others without any fixed infrastructure. Due to frequent topology changes, VANET suffers from challenges like routing and data dissemination between the vehicles. This research work develops an efficient technique called Mean shift Margin Boost Clustering Based Multivariate Dolphin Swarm Optimized Routing (MMBC-MDSOR) technique for improving the routing and reliable data dissemination in VANET. A mean-shift margin boost clustering is an ensemble clustering technique to divide the total network into a number of groups. Each group comprises the number of vehicle nodes by the ensemble method uses the iterative Gaussian kernelized mean shifted clustering technique to assign each vehicle towards the closest cluster centroid based on the different stability parameters such as vehicles density, direction, distance and velocity to form a cluster head. By using cluster head, the data communication is controlled between vehicle nodes as well as the end to end delay is reduced. Multivariate Dolphin Swarm Optimized Routing (MDSOR) is the cluster based optimization to select the optimal cluster head based on fitness function in terms of distance, signal strength and bandwidth. Then the optimal route path between source to destination is identified to disseminate the messages. The designed MDSOR in MMBC-MDSOR increases the reliability, throughput and minimizes packet drop ratio. The above technique can be applied wherever there is high congestion on the road due to the failure of optimal link discovery and data distribution between the vehicles in emergency condition. The simulation result shows that the MMBC-MDSOR Technique can enhance the reliability, throughput and also minimizes the end to end delay and packet drop ratio in VANET as compared to state-of-the-art works

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Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

Javed¹,

Jalil

Moqurrab

et al. 2020

Trans Emerging Tel Tech

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The key characteristic of smart cities (ie, connectivity and intelligence) has enabled connected vehicles to work together to accomplish complex jobs that they are unable to perform individually. Connectivity not only being an inevitable blessing but also poses growing cybersecurity challenges for connected vehicles. The overall risk of connected vehicles is wide as the cybercriminals are nowadays applying versatile approaches (botnets, phishing, zero-days, rootkits, etc) to disrupt their communication. The botnet, capable of launching distributed denial of service attack, is a potential threat for any connected ecosystem due to its excessive capabilities of using maximum compromised devices. Hence, the detection of botnet attacks has become a pinnacle point for cybersecurity analysts. Existing studies lack in detecting botnet at the earliest and accurately. This article presents an approach to detect the botnet attacks at an early stage and accurately utilizing typical network traffic and temporal features. The proposed approach use machine learning algorithms for the detection of botnet attack(s) and present a detailed comparison of the decision tree, probabilistic neural network, sequential minimal optimization, and Adaboost classifiers, as well as comparison of the proposed approach with existing studies in this domain, is also done. Furthermore, it investigates the role of temporal features for botnet detection. The proposed approach accomplishes the promising true positive rate of 99.7%. Results show that our methodology is efficient compared with the existing studies. INTRODUCTIONThe concept of a smart city is primarily composed of information and communication technologies (ICT), to advance the quality of services such as connectivity, energy, transportation, and others. Especially in transportation the continuous vehicle to infrastructure (V2I), vehicle-to-sensors (V2S), vehicle-to-pedestrians, vehicle-to-vehicle (V2V) and the vehicle-to-cloud (V2C) services in smart cities are becoming essential for safety and nonsafety applications. Smart cities provide a channel to the driver, vehicle, infrastructure, and cloud to connect. 1,2 Smart cities produce a huge amount of data collected through IoT devices for further analysis that brings security issues with regard to communicating entities. [3][4][5][6][7] IoT devices (ie, smartphones, smartwatches, smart TV) have become a need of our daily lives. People around the globe are connected using personal smart devices and use them to perform many routine tasks daily. [8][9][10] Security in this current era of the connected world is most important as a single cyberattack can result in loss of important Abdul Rehman Javed and Zunera Jalil contributed equally to this study.

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Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications

Cited by 32 publications

References 7 publications

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Margin Boost Clustering based Multivariate Dolphin Swarm Optimization for Routing and Reliable Data Dissemination in VANET

Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles

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