A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

Cluster Computing

Volumn 14, Issue 4, 2011, Pages 311-324

  Akbari Torkestani, Javad ^a   Meybodi, Mohammad Reza ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Learning automata; MANET; Network clustering

Indexed keywords

AUTOMATA THEORY; DISTRIBUTION FUNCTIONS; MOBILE AD HOC NETWORKS; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; RANDOM VARIABLES; STOCHASTIC SYSTEMS;

CLUSTER FORMATIONS; CONTROL MESSAGE OVERHEADS; LEARNING AUTOMATA; MANET; MOBILITY PARAMETERS; NETWORK CLUSTERING; NUMBER OF CLUSTERS; WIRELESS MOBILE AD-HOC NETWORKS;

CLUSTERING ALGORITHMS;

EID: 81355149899     PISSN: 13867857     EISSN: 15737543     Source Type: Journal    
DOI: 10.1007/s10586-011-0161-z     Document Type: Article

References (38)

1. Akbari Torkestani, J., Meybodi, M. R.: Approximating the minimum connected dominating set in stochastic graphs based on learning automata. In: Proceedings of International Conference on Information Management and Engineering (ICIME 2009), Kuala Lumpur, Malaysia, April 3-5 (2009).
2. Akbari Torkestani, J., Meybodi, M. R.: Solving the minimum spanning tree problem in stochastic graphs using learning automata. In: Proceedings of International Conference on Information Management and Engineering (ICIME 2009) Kuala Lumpur, Malaysia, April 3-5 (2009).
3. Akbari Torkestani, J., Meybodi, M. R.: Clustering the wireless ad hoc networks: a distributed learning automata approach. J. Parallel Distrib. Comput. 70, 394-405 (2010).
4. Akbari Torkestani, J., Meybodi, M. R.: Weighted steiner connected dominating set and its application to multicast routing in wireless MANETs. Wireless Personal Communications (2010). doi: 10. 1007/s11277-010-9936-4.
5. Akbari Torkestani, J., Meybodi, M. R.: A learning automata-based cognitive radio for clustered wireless ad-hoc networks. J. Netw. Syst. Manag. (2010, in press).
6. Akbari Torkestani, J., Meybodi, M. R.: An intelligent backbone formation algorithm in wireless ad hoc networks based on distributed learning automata. J. Comput. Netw. 54, 826-843 (2010).
7. Akbari Torkestani, J., Meybodi, M. R.: A new vertex coloring algorithm based on variable action-set learning automata. J. Comput. Inf. 29(3), 1001-1020 (2010).
8. Artail, H., Antoun, R., Fawaz, K.: CRUST: implementation of clustering and routing functions for mobile ad hoc networks using reactive tuple-spaces. Ad Hoc Netw. 7, 1064-1081 (2009).
9. Baker, D. J., Ephremides, A.: The architectural organization of a mobile radio network via a distributed algorithm. IEEE Trans. Commun. 29(11), 1694-1701 (1981).
10. Basagni, S.: Distributed clustering for ad-hoc networks. In: Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks (I-SPAN'99), pp. 310-315 (1999).
11. Basu, P., Khan, N., Little, T. D. C.: Mobility-based metric for clustering in mobile ad-hoc networks. In: Workshop on Distributed Computing Systems, pp. 413-418 (2001).
12. Chatterjee, M., Das, S., Turgut, D.: WCA: a weighted clustering algorithm for mobile ad-hoc networks. Cluster Comput. 5, 193-204 (2002).
13. Chen, A., Cai, Z., Hu, D.: Clustering in mobile ad hoc network based on neural network. J. Cent. South Univ. Technol. 13(6), 699-702 (2006).
14. Elhdhili, M., Azzouz, L., Kamoun, F.: CASAN: clustering algorithm for security in ad hoc networks. Comput. Commun. 31, 2972-2980 (2008).
15. Er, I. I., Seah, W. K. G.: Clustering overhead and convergence time analysis of the mobility-based multi-hop clustering algorithm for mobile ad hoc networks. J. Comput. Syst. Sci. 72, 1144-1155 (2006).
16. Er, I., Seah, W. K. G.: Performance analysis of mobility-based D-hop (MobDHop) clustering algorithm for mobile ad-hoc networks. Comput. Netw. 50, 3375-3399 (2006).
17. Esnaashari, M., Meybodi, M. R.: Data aggregation in sensor networks using learning automata. Wirel. Netw. 16(3), 687-699 (2010).
18. Esnaashari, M., Meybodi, M. R.: Dynamic point coverage problem in wireless sensor networks: a cellular learning automata approach. J. Ad hoc Sens. Wirel. Netw. 10(2-3), 193-234 (2010).
19. Esnaashari, M., Meybodi, M. R.: A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Comput. Netw. (2010, to appear).
20. Garcia Nocetti, F., Solano Gonzalez, J., Stojmenovic, I.: Connectivity based k-hop clustering in wireless networks. Telecommun. Syst. 22(1-4), 205-220 (2003).
21. Gerla, M., Tsai, J.: Multicluster, mobile, multimedia radio network. ACM/Baltzer J. Wirel. Netw. 1(3), 255-265 (1995).
22. Ghosh, R., Basagni, S.: Mitigating the impact of node mobility on ad-hoc clustering. J. Wirel. Commun. Mob. Comput. 8, 295-308 (2008).
23. IEEE Computer Society LAN MAN Standards Committee: Wireless LAN Medium Access Protocol (MAC) and Physical Layer (PHY) specification, IEEE Standard 802. 11-1997. The Institute of Electrical and Electronics Engineers, New York (1997).
24. Konstantopoulos, C., Gavalas, D., Pantziou, G.: Clustering in mobile ad-hoc networks through neighborhood stability-based mobility prediction. Comput. Netw. 52, 1797-1824 (2008).
25. Lin, C. R., Gerla, M.: Adaptive clustering for mobile wireless networks. IEEE J. Sel. Areas Commun. 15(7), 1265-1275 (1997).
26. Narendra, K. S., Thathachar, K. S.: Learning Automata: An Introduction. Prentice-Hall, New York (1989).
27. Palit, R., Hossain, E., Thulasiraman, P.: MAPLE: a framework for mobility-aware pro-active low energy clustering in ad-hoc mobile wireless networks. Wirel. Commun. Mob. Comput. 6, 773-789 (2006).
28. Shahzad, W., Aslam Khan, F., Basit Siddiqui, A.: Clustering in mobile ad hoc networks using comprehensive learning particle swarm optimization (CLPSO). Commun. Netw. 56, 342-349 (2009).
29. Thathachar, M. A. L., Harita, B. R.: Learning automata with changing number of actions. IEEE Trans. Syst. Man Cybern. SMG-17, 1095-1100 (1987).
30. Thathachat, M. A. L., Sastry, P. S.: A hierarchical system of learning automata that can learn the globally optimal path. Inf. Sci. 42, 743-766 (1997).
31. Wang, S.-C., Pan, H.-H., Yan, K.-Q., Lo, Y.-L.: A unified framework for cluster manager election and clustering mechanism in mobile ad hoc networks. Comput. Stand. Interfaces 30, 329-338 (2008).
32. Yang Yu, J., Chong, P.: An efficient clustering scheme for large and dense mobile ad hoc networks (MANETs). Comput. Commun. 30, 5-16 (2006).
33. Zhang, Y., Mee Ng, J., Ping Low, C.: A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks. Comput. Commun. 32, 189-202 (2009).
34. Akbari Torkestani, J., Meybodi, M. R.: Learning automata-based algorithms for finding minimum weakly connected dominating set in stochastic graphs. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 18(6) (2010).
35. Akbari Torkestani, J., Meybodi, M. R.: A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs. J. Supercomput. (2010, in press).
36. Akbari Torkestani, J., Meybodi, M. R.: A cellular learning automata-based algorithm for solving the vertex coloring problem. J. Expert Syst. Appl. (2011, in press).
37. Akbari Torkestani, J., Meybodi, M. R.: Mobility-based multicast routing algorithm in wireless mobile ad hoc networks: a learning automata approach. J. Comput. Commun. 33(6), 721-735 (2010).
38. Akbari Torkestani, J., Meybodi, M. R.: An efficient cluster-based CDMA/TDMA scheme for wireless mobile ad-hoc networks: a learning automata approach. J. Netw. Comput. Appl. 33, 477-490 (2010).