Algorithms for stable clustering in vanets (vehicular ad hoc networks)

Σημείωση: διατίθεται συμπληρωματικό υλικό σε ξεχωριστό αρχείο

Mobility Adaptive Density Connected Clustering Approach in Vehicular Ad Hoc Networks

Clustering is one of the popular topology management approaches that can positively influence the performance of networks. It plays significant role in VANETs. However, VANETs having highly mobile nodes lead to dynamic topology and hence, it is very difficult to construct stable clusters. More homogeneous environment produces more stable clusters. Homogeneous neighbourhood for a vehicle is strongly driven by density and standard deviation of average relative velocity of vehicles in its communication range. So, we propose Mobility Adaptive Density Connected Clustering Algorithm (MADCCA), a density based clustering algorithm. The Cluster Heads (CHs) are selected based on the standard deviation of average relative velocity and density matrices in their neighbourhood. Vehicle, which is having more homogeneous environments, will become the cluster heads and rest of the vehicles in their communication range will be the Cluster Members (CMs). The simulation results demonstrates the better performance of MADCCA over other clustering algorithms new ALM and MOBIC

A Comparative Survey of VANET Clustering Techniques

© 2016 Crown. A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles - most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming - the lack of realistic vehicular channel modeling - is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated

Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research

Vehicular communication networks received good consideration and focusing on diverse researchers in the latest years. Vehicular Adhoc Networks (VANETs) represents a developed type of an effective communication technology to facilitate the process of information dissemination among vehicles. VANETs established the cornerstone to develop the Intelligent Transport Systems (ITS). The great challenging task in routing the messages in VANETs is related to the different velocities of the moving vehicles on the streets in addition to their sparse distribution. Clustering approach is broadly used to report this challenge. It represents the mechanism of the alliance the vehicles based on certain metrics such as velocity, location, density, direction and lane position. This paper is to investigate and analyze several challenges and their present solutions which based on different developed clustering approaches based on the affinity propagation algorithm. This paper isaim to present a complete taxonomy on vehicles clustering and analyzing the existing submitted proposals in literature based on affinity propagation. Presenting and analyzing the submitted proposals will provide these domain researchers with a good flexibility to select or apply the suitable approach to their future application or research activities. To prepare this paper in a systematic manner, a total of 1444 articles concerning the Affinity Propagation in clustering published in the era of 2008 to 2019 were collected from the reliable publishing sources namely (ScienceDirect, IEEE Xplore, and SCOPUS). Due to their relevance, applicability, generality level and comprehensiveness, only nineteen articles among the collected articles were assigned and eventually analyzed in a systematic review method.A considerable success has been achieved in revealing the essential challenges and necessities for clustering based affinity Propagation in VANETs to guide the researchers in their upcoming investigations. This paper also contributes in dealing with open problems issues, challenges and guidelines for the upcoming investigations

Implementation of CAVENET and its usage for performance evaluation of AODV, OLSR and DYMO protocols in vehicular networks

Vehicle Ad-hoc Network (VANET) is a kind of Mobile Ad-hoc Network (MANET) that establishes wireless connection between cars. In VANETs and MANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. In MANETs, the Random Waypoint (RW) model is used as a simulation model for generating node mobility pattern. On the other hand, in VANETs, the mobility patterns of nodes is restricted along the roads, and is affected by the movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based VEhicular NETwork). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We improved CAVENET and implemented some routing protocols. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.Peer ReviewedPostprint (published version

Reliable and efficient data dissemination schemein VANET: a review

Vehicular ad-hoc network (VANET), identified as a mobile ad hoc network MANETs with several added constraints. Basically, in VANETs, the network is established on the fly based on the availability of vehicles on roads and supporting infrastructures along the roads, such as base stations. Vehicles and road-side infrastructures are required to provide communication facilities, particularly when enough vehicles are not available on the roads for effective communication. VANETs are crucial for providing a wide range of safety and non-safety applications to road users. However, the specific fundamental problem in VANET is the challenge of creating effective communication between two fast-moving vehicles. Therefore, message routing is an issue for many safety and non-safety of VANETs applications. The challenge in designing a robust but reliable message dissemination technique is primarily due to the stringent QoS requirements of the VANETs safety applications. This paper investigated various methods and conducted literature on an idea to develop a model for efficient and reliable message dissemination routing techniques in VANET

Enhanced Load Balanced Clustering Technique for VANET Using Location Aware Genetic Algorithm

The vehicular Adhoc Network has unique charac-teristics of frequent topology changes, traffic rule-based node movement, and speculative travel pattern. It leads to stochastic unstable nature in forming clusters. The re-liable routing process and load balancing are essential to improve the network lifetime. Cluster formation is used to split the network topology into small structures. The reduced size network leads to accumulating the topology information quickly. Due to the absence of centralised management, there is a pitfall in network topology man-agement and optimal resource allocation, resulting in ineffective routing. Hence, it is necessary to develop an effective clustering algorithm for VANET. In this paper, the Genetic Algorithm (GA) and Dynamic Programming (DP) are used in designing load-balanced clusters. The proposed Angular Zone Augmented Elitism-Based Im-migrants GA (AZEIGA) used elitism-based immigrants GA to deal with the population and DP to store the out-come of old environments. AZEIGA ensures clustering of load-balanced nodes, which prolongs the network lifetime. Experimental results show that AZEIGA works appreciably well in homogeneous resource class VANET. The simulation proves that AZEIGA gave better perfor-mance in packet delivery, network lifetime, average de-lay, routing, and clustering overhead

V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning

To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios