A New Aggregate Local Mobility &amp;#40;ALM&amp;#41; Clustering Algorithm for VANETs

Souza, Evandro de; Nikolaidis, Ioanis; Gburzyński, Paweł

doi:10.1109/icc.2010.5501789

Cited by 109 publications

(69 citation statements)

References 9 publications

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“…The Aggregate Local Mobility (ALM) method incorporates the Received Signal Strength (RSS) as a metric in computing the distance between the sender and the receiver [20]. But this method does not work well in computation of the distance in VANETs [21].…”

Section: Related Workmentioning

confidence: 99%

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

Hussain¹,

Chen²

2017

ijacsa

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Abstract-Vehicular Ad-hoc Network (VANETs) is the specific form of Mobile ad-hoc networking (MANETs) in which high dynamic nodes are utilized in carrying out the operations. They are mainly used in urban areas for safety traveling. Clustering algorithms are used for clustering the vehicles that are in the range of the network as VANET consists of a great amount of traffic. A clustering head node is used specified through a procedure to collect all information from the surroundings. This study introduced a new method for cluster head selection by using the K-Mean and Floyd-Warshall algorithms. The proposed technique first divided the points for vehicle groups while the Floyd-Warshall algorithm calculated all pairs of shortest distance for every vehicle within the defined cluster. A vehicle with the smallest average distance among a cluster is chosen as the cluster head. The Floyd-Warshall algorithm overall selects a centralized vehicle as a cluster head, hence its stability time will improve significantly.

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Section: Related Workmentioning

confidence: 99%

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

Hussain¹,

Chen²

2017

ijacsa

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“…Having this dynamic characteristic of a VANET in mind, a good clustering method that can reassure good stability and high cluster lifetime must incorporate many different factors during both the cluster formation and cluster maintenance processes [63,81]. In addition, an effective clustering method has to take into account signal fading and channel interference.…”

Section: Social Clustering Of Vehiclesmentioning

confidence: 99%

“…Furthermore, central nodes can be elected as the clusterhead of groups that are created on the fly. A clusterhead may act as a relay node for traffic coming or destined to different clusters or as a relay node for intra-cluster communication [63].…”

Section: Social Network Analysis In Siovsmentioning

confidence: 99%

Social Internet of Vehicles for Smart Cities

Μαγλαράς

Al-Bayatti

et al. 2016

JSAN

134

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Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

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“…In particular, this algorithm considers speed, location, and direction via the Global Positioning System (GPS) or other similar services to form clusters. The aggregate local mobility (ALM) algorithm [5] is a new beacon-based clustering scheme that aims to extend the lifetime of clusters by using ALM to decide cluster reorganization. Meanwhile, Rawashdeh and Mahmud [7] considered speed and relative direction to present a novel speed-overlapped clustering algorithm for highways.…”

Section: Related Workmentioning

confidence: 99%

“…Considerable research has explored clustering algorithms for VANETs to satisfy the requirements of their new features [3][4][5][6][7][8][9][10]. Most of these studies are based on one-hop clustering, which only allows communication between a cluster member (CM) and its cluster head (CH) with one-hop distance.…”

Section: Introductionmentioning

confidence: 99%

Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow

Chen

Fang

Shi

et al. 2015

J Wireless Com Network

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Vehicular ad hoc networks (VANETs) have become important components of metropolitan area networks, and clustering for VANETS provides many advantages. However, the stability of current clustering algorithms exhibits poor robustness because a VANET is a highly dynamic scenario. In this study, a novel multi-hop clustering scheme for VANETs, which generates cluster heads (CHs) via neighborhood follow relationship between vehicles, is proposed. The scheme is based on a reasonable assumption that a vehicle cannot certainly identify which vehicle in its multi-hop neighbors is the most suitable to be its CH, but it can easily grasp which vehicle in one-hop distance is the most stable and similar with it, and thus, they most likely belong to the same cluster. Consequently, a vehicle can choose its CH by following the most stable vehicle. The relative mobility between two vehicles combining the gains based on the followed number and the historical following information enables a vehicle to select which target to follow. Extensive simulation experiments are conducted to validate the performance of the proposed clustering scheme.

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A New Aggregate Local Mobility (ALM) Clustering Algorithm for VANETs

Cited by 109 publications

References 9 publications

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

Cluster Formation and Cluster Head Selection Approach for Vehicle Ad-Hoc Network (VANETs) using K-Means and Floyd-Warshall Technique

Social Internet of Vehicles for Smart Cities

Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow

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