An Adaptable Mobility-Aware Clustering Algorithm in vehicular networks

Morales, Mildred Madai Caballeros; Hong, Choong Seon; Bang, Young-Cheol

doi:10.1109/apnoms.2011.6077004

Cited by 63 publications

(38 citation statements)

References 7 publications

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“…The clusters group vehicles based on the predicted traveling path and intersections. Morales et al [27] propose an adaptable mobility-aware clustering algorithm that can follow the mobility pattern of the network based on vehicle destination. Rawashdeh et al [28] propose a speed-overlapped clustering technique on highways by using vehicular speed and relative movement direction.…”

Section: Collaborative Topologymentioning

confidence: 99%

Opportunistic fleets for road event detection in vehicular sensor networks

Zeng

Vasilakos

2015

Wireless Netw

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High mobility and variable road surface condition brings big challenges for road event detection by using vehicular sensor networks (VSNs). VSNs are datacentric networks, so that the efficient collaborations among opportunistic topology structure in the network can help to improve the performance. In this paper, we present the behavior-aware fleet construction schemes to form and maintain the opportunistic fleet topology, and then to make fleet based data collaboration. The fleet formation scheme is to group vehicles according to behavior similarities and stability. The fleet maintenance scheme includes the periodic update and deputy selection to adapt the fleet topology. The collaboration within the fleet is executed in the way of ''follower-to-leader'' with data aggregation among behavior-aware vehicles. Our analysis show the schemes are with low overhead and in an on-line and decentralized way, which is well suited for VSNs. We made simulations to testify the scheme performance for road event detection applications. The results show that our schemes achieve better accuracy for road event detection when compared with the other related work.

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Section: Collaborative Topologymentioning

confidence: 99%

Opportunistic fleets for road event detection in vehicular sensor networks

Zeng

Vasilakos

2015

Wireless Netw

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“…The vehicles that are moving at high speed are regrouped into one cluster, while the vehicles moving at low speed are grouped into another cluster. An Adaptable Mobility-Aware Clustering Algorithm based on Destination positions, called AMACAD, is proposed and evaluated by Morales et al [6]. The goal of this work has been to develop a clustering protocol with an efficient message exchange mechanism, which improves the clustering stability in VANETs.…”

Section: B Related Workmentioning

confidence: 99%

Using road IDs to enhance clustering in vehicular ad hoc networks

Hadded

Mühlethaler

Zagrouba

et al. 2015

2015 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Vehicular ad hoc networks (VANETs) where vehicles act as mobile nodes is an instance of Mobile Ad hoc NETworks (MANETs), which are essentially developed for intelligent transportation systems. A challenging problem when designing communication protocols in VANETs is coping with high vehicle mobility, which causes frequent changes in the network topology and leads to frequent breaks in communication. The clustering technique is being developed to reduce the impact of mobility between neighboring vehicles. In this paper, we propose an Adaptive Weighted Cluster Protocol for VANETs, which is a road map dependent and uses road IDs and movement direction in order to make the clusters structure as stable as possible. The experimental results reveal that AWCP outperforms four other most commonly used clustering protocols in terms of control packet overhead, the packet delivery ratio, and the average cluster lifetime, which are the most usual metrics used for comparing performance.

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“…Most frequently, the MOBIC is used as a de facto reference for comparing VANET clustering algorithms [13,[22][23][24][25], so it was a logical choice to use it in our case as well. This allows comparative performance comparison to other clustering algorithms that also use MOBIC as a reference.…”

Section: Simulation and Test Scenariosmentioning

confidence: 99%

A Multihoming Clustering Algorithm for Vehicular Ad Hoc Networks

Vodopivec

Bešter

Kos

2014

International Journal of Distributed Sensor Networks

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Clustering in vehicular ad hoc networks is an effective approach to make dynamic wireless vehicular sensor networks more manageable and stable. To make vehicle clustering applicable everywhere regardless of the provided infrastructure, vehicles must rely only on themselves and must not take any supporting services, such as location or external communication services, for granted. In this paper, we propose a new clustering metric and a clustering algorithm with multihoming support. It relies only on the vehicle's ability to send and receive wireless packets which identify the vehicle relationship. Clusters are created with redundant connections between nodes to increase the communication reliability in case of topological changes and the cluster creation process is also inverted compared to other algorithms. The presented solution is verified and compared to MOBIC with the use of ns-3 and SUMO simulation tools. Simulation results have confirmed the expected behavior and show that our algorithm achieves better node connectivity and cluster stability than the former.

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An Adaptable Mobility-Aware Clustering Algorithm in vehicular networks

Cited by 63 publications

References 7 publications

Opportunistic fleets for road event detection in vehicular sensor networks

Opportunistic fleets for road event detection in vehicular sensor networks

Using road IDs to enhance clustering in vehicular ad hoc networks

A Multihoming Clustering Algorithm for Vehicular Ad Hoc Networks

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