Top

Wireless Personal Communications

Published in:

08-05-2019

Dynamic Clustering Mechanism to Avoid Congestion Control in Vehicular Ad Hoc Networks Based on Node Density

Authors: R. Regin, T. Menakadevi

Published in: Wireless Personal Communications | Issue 4/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Due to the rapid growth in wireless communications, vehicular ad-hoc networks (VANETs) face many challenges over wireless communication networks. Nowadays VANETs have become a major research and they have incredible resources to improve road traffic efficiency, safety, convenience and comfort to both drivers and passengers etc. Congestion occurs when the vehicles are in the dense part while the network node is carrying more data than it can handle. The proposed DBDC: density based dynamic clustering devices determine node density of the precise location in a lane and provide a proactive solution for congestion. To trigger the congestion control process in the cluster, we use average vehicle density threshold which is calculated using trained dataset. If the node density is greater than the threshold value, the dynamic clustering process is triggered. Our simulation result indicates that the overall performance of our scheme is better than multi agent dynamic clustering and vehicular weighted clustering algorithm in terms of average cluster number, cluster head duration and average cluster member duration.

previous article Availability of Cellular Systems Based on Space Domain

next article Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Karagiannis, G., Altintas, O., Ekici, E., Heijenk, G., Jarupan, B., Lin, K., et al. (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 13, 584–616.CrossRef

Zeadally, S., Hunt, R., Chen, Y.-S., Irwin, A., & Hassan, A. (2012). Vehicular ad hoc networks (VANETS): Status, results, and challenges. Telecommunication Systems, 50, 217–241.CrossRef

Stanica, R., Chaput, E., & Beylot, A. (2011). Local density estimation for contention window adaptation in vehicular networks. In IEEE 22nd international symposium on personal indoor and mobile radio communications (PIMRC), Sept. 2011 (pp. 730–734).

Sanguesa, J. A., Fogue, M., Garrido, P., Martinez, F. J., Cano, J.-C., Calafate, C. T., et al. (2013). An infrastructureless approach to estimate vehicular density in urban environments. Sensors, 13, 2399–2406.CrossRef

Dimitrakopoulos, G., & Demestichas, P. (2010). Intelligent transportation systems. Vehicular Technology Magazine, IEEE, 5(1), 77–84.CrossRef

Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J.-C., Calafate, C. T., et al. (2013). Road side unit deployment: A density-based approach. IEEE Intelligent Transportation Systems Magazine, 5(3), 30–39.CrossRef

Kumar, V., Mishra, S., & Chand, N. (2013). Applications of VANETs: Present and future. Communications and Network, 5, 12–15.CrossRef

Yair, A., & Segal, M. (2011). Near-optimal, reliable and self-organizing hierarchical topology in VANET. In: Eighth ACM international workshop on vehicular internetworking (pp. 79–80).

Song, T., Xia, W., Song, T., & Shen, L. (2010). A cluster-based directional routing protocol in VANET. In 12th IEEE international conference on communication technology (ICCT) (pp. 1172–1175).

10.

Wiegel, B., Gunter, Y., & Grossmann, H. (2007). Cross-layer design for packet routing in vehicular ad hoc networks. In IEEE 66th vehicular technology conference VTC Fall (pp. 2169–2173).

11.

Zhang, Z., Boukerche, A., & Pazzi, R. (2011). A novel multi-hop clustering scheme for vehicular ad-hoc networks. In Proceedings of the 9th ACM international symposium on mobility management and wireless access (pp. 19–26).

12.

Su, H., & Zhang, X. (2007). Clustering-based multichannel MAC protocols for QoS provisionings over vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 56(6), 3309–3323.CrossRef

13.

Daeinabi, A., Pour Rahbar, A. G., & Khademzadeh, A. (2011). VWCA: An efficient clustering algorithm in vehicular ad hoc networks. Journal of Network and Computer Applications, 34(1), 207–222.CrossRef

14.

Wolny, G. (2008). Modified DMAC clustering algorithm for VANETs. In Systems and networks communications, ICSNC (pp. 268–273).

15.

Hassanabadi, B., Shea, C., Zhang, L., & Valaee, S. (2014). Clustering in vehicular ad hoc networks using affinity propagation. Ad Hoc Networks, 13, 535–548.CrossRef

16.

Souza, E., Nikolaidis, I., & Gburzynski, P. (2010). A new aggregate local mobility ALM; Clustering algorithm for VANETs. In IEEE International conference on communications (ICC) (pp. 1–5).

17.

Rawashdeh, Z. Y., & Mahmud, S. (2012). A novel algorithm to form stable clusters in vehicular ad hoc networks on highways. In EURASIP journal on wireless communications and networking (p. 15).

18.

Wang, Z., Liu, L., Zhou, M., & Ansari, N. (2008). A position-based clustering technique for ad hoc intervehicle communication. In IEEE transactions on systems, man, and cybernetics, part C: applications and reviews.

19.

Basagni, S. (1999). Distributed clustering for ad hoc networks. In Proceedings of the fourth international symposium on parallel architectures, algorithms, and networks (I-SPAN’99), Perth/Fremantle, Australia (pp. 310–315).

20.

Basu, P., Khan, N., & Little, T. (2001). A mobility based metric for clustering in mobile ad hoc networks. In Proceedings of distributed computing systems workshop, Vol. 2001, Phoenix/Mesa, Arizona (pp. 413–418).

21.

McDonald, A., & Znati, T. (1999). A mobility-based framework for adaptive clustering in wireless adhoc networks. IEEE Journal on Selected Areas in Communications, 17(8), 1466–1487.CrossRef

22.

Kuklinski, S., & Wolny, G. (2009). Density based clustering algorithm for VANETs. International Journal of Internet Protocol Technology, 4, 149–157.CrossRef

23.

Bakhoya, M., Gaber, J., & Lorenz, P. (2011). An adaptive approach for information dissemination in vehicular ad hoc networks. Journal of Network and Computer Applications, 34, 1971–1978.CrossRef

24.

Darwish, T., & Bakar, K. A. (2015). Traffic density estimation in vehicular ad hoc networks: A review. Pervasive Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310.

25.

Kakkasageri, M. S., & Manvi, S. S. (2012). Multiagent driven dynamic clustering of vehicles in VANETs. Journal of Network and Computer Applications, 35, 1771–1780.CrossRef

26.

Daeinabi, A., Rahbar, A. G. P., & Khademzadeh, A. (2011). VWCA: An efficient clustering algorithm in vehicular ad hoc networks. Journal of Network and Computer Applications, 34, 207–222.CrossRef

27.

Liu, X., Fan, Z., & Shi, L. (2007). Securing vehicular ad hoc networks. In Second conference on international pervasive computing and applications, Amsterdam, The Netherlands (pp. 424–432).

28.

Beckmann, M., McGuire, C. B., & Winsten, C. B. (1955). Studies in the economics of transportation. Number RM-1488. Yale University Press.

29.

Bertini, R. L. (2003). Toward the systematic diagnosis of freeway bottleneck activation. In Proceedings of the IEEE 6th annual conference on intelligent transportation systems, Shanghai, China.

30.

Bovy, P. L., & Thijs, R. (Eds.). (2000). Estimators of travel time for road networks—New developments, evaluation results, and applications. Delft: Delft University Press.

31.

Brilon, W. (2000). Traffic flow analysis beyond traditional methods. In Transportation research circular EC018: 4th international symposium on highway capacity (pp. 26–41). Transportation Research Board and National Research Council.

32.

Buckley, D. J., & Yagar, S. (1974). Capacity funnels near onramps. In D. J. Buckley (Ed.), Proceedings of the 6th international symposium on transportation and traffic theory, London, United Kingdom (pp.105–123).

Title: Dynamic Clustering Mechanism to Avoid Congestion Control in Vehicular Ad Hoc Networks Based on Node Density
Authors: R. Regin
T. Menakadevi
Publication date: 08-05-2019
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2019
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-019-06366-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2019

Scalable Models for Redundant Data Flow Analysis in Online Social Networks

Road State Novel Detection Approach in VANET Networks Based on Hadoop Ecosystem

A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications

Design of a Highly Linear Gain Stage with Complementary Derivative Superposition Technique

Channel Estimation in Massive MIMO Systems Using a Modified Bayes-GMM Method

Novel Approach for Mobile Based App Development Incorporating MAAF