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A Study on Cluster Stabilization Strategies for Human-Driven MANETs

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Abstract

Clustering is a commonly used method to adequately manage the network resources provided by mobile ad hoc networks (MANETs). In order to work appropriately in these networks, clustering protocols integrate stabilizing strategies that cope with topological changes caused by node motion. Although there is a number of studies related to clustering, cluster stability remains as a critical feature that has been barely addressed. It is not known, for instance, how stability of clustering protocols is affected when network nodes move following human mobility patterns (human-driven MANETs). The aim of this paper is to explore this open issue in order to improve the performance of clustering protocols in a human-driven environment and, in this way, to advance MANET research one step toward the deployment of real networks. To this end, the performance of several stabilizing strategies is assessed considering a network scenario where nodes roam according to the self-similar least-action walk (SLAW) model, which integrates several statistical features of human motion. Additionally, this work proposes to classify the stabilizing strategies into clusterhead-election based strategies, strategies based on relaxed-validity policies and strategies controlling the number of clusters. Our findings show that clustering protocols have to integrate flexible policies in order to improve stability as well as strategies dealing with burst reaffiliations and big-sized clusters.

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References

  1. Gupta, P., & Kumar, P. R. (2000). The capacity of wireless networks. IEEE Transactions on Information Theory, 46(2), 388–404. doi:10.1109/18.825799.

    Article  MathSciNet  MATH  Google Scholar 

  2. Chinara, S., & Rath, S. K. (2009). A survey on one-hop clustering algorithms in mobile ad hoc networks. Journal Network System Management, 17(1–2), 183–207. doi:10.1007/s10922-009-9123-7.

    Article  Google Scholar 

  3. Yu, J. Y., & Chong, P. H. (2005). A survey of clustering schemes for mobile ad hoc networks. IEEE Communications Surveys Tutorials, 7(1), 32–48. doi:10.1109/COMST.2005.1423333.

    Article  Google Scholar 

  4. Dang, H., & Wu, H. (2010). Clustering and cluster-based routing protocol for delay-tolerant mobile networks. IEEE Transactions on Wireless Communications, 9(6), 1874–1881. doi:10.1109/TWC.2010.06.081216.

    Article  Google Scholar 

  5. Chen, K., Shen, H., & Zhang, H. (2014). Leveraging social networks for P2P content-based file sharing in disconnected MANETs. IEEE Transactions on Mobile Computing, 13(2), 235–249. doi:10.1109/TMC.2012.239.

    Article  Google Scholar 

  6. Li, H., Bok, K.S., & Yoo, J.S. (2011). An efficient clustering method for unstructured mobile peer-to-peer networks. In Proceedings of the 2011 ACM symposium on research in applied computation (RACS), Miami, USA (pp 124–129).

  7. Ephremides, A., Wieselthier, J. E., & Baker, D. J. (1987). A design concept for reliable mobile radio networks with frequency hopping signaling. Proceedings of the IEEE, 75(1), 56–73. doi:10.1109/PROC.1987.13705.

    Article  Google Scholar 

  8. Karaoglu, B., Numanoglu, T., & Heinzelman, W. (2009). Adaptation of TDMA parameters based on network conditions. In IEEE wireless communications and networking conference (WCNC 2009) (pp. 1–6). Budapest, Hungary.

  9. Chaba, Y., Singh, Y., & Joon, M. (2009). Performance evaluation and analysis of cluster based routing protocols in manets. In International conference on advances in computing, control, telecommunication technologies (ACT ’09) (pp. 64–66). Trivandrum, Kerala.

  10. Enciso Quispe, L., & Mengual Galán, L. (2014). Behavior of ad hoc routing protocols, analyzed for emergency and rescue scenarios, on a real urban area. Expert Systems with Applications, 41(5), 2565–2573. doi:10.1016/j.eswa.2013.10.004.

    Article  Google Scholar 

  11. Venkanna, U., & Leela Velusamy, R. (2016). TEA-CBRP: Distributed cluster head election in MANET by using AHP. Peer-to-Peer Networking and Applications, 9(1), 159–170. doi:10.1007/s12083-014-0320-0.

    Article  Google Scholar 

  12. Jiang, M., Li, J., & Tay, Y. C. (1999). Cluster based routing protocol (CBRP). INTERNET-DRAFT, IETF.

  13. Perkins, C.E., Belding-Royer, E.M. (1999). Ad-hoc on-demand distance vector routing. In Proceedings on the second IEEE workshop on mobile computing systems and applications (WMCSA), New Orleans, USA (pp 90–100).

  14. Basu, P., Khan, N., & Little, T. D. C. (2001). A mobility based metric for clustering in mobile ad hoc networks. In International conference on distributed computing systems workshop (CDCS 01) (pp. 413–418). Mesa, USA.

  15. Gerla, M., & Tsai, J Tc. (1995). Multicluster, mobile, multimedia radio network. Journal of Wireless Networks, 1, 255–265.

    Article  Google Scholar 

  16. Wu, J., Dai, F., Gao, M., & Stojmenovic, I. (2002). On calculating power-aware connected dominating sets for efficient routing in ad hoc wireless networks. Journal of Communications and Networks, 4(1), 59–70. doi:10.1109/JCN.2002.6596934.

    Article  Google Scholar 

  17. Aissa, M., & Belghith, A. (2014). Quality of clustering in mobile ad hoc networks. Procedia Computer Science, 32, 245–252. doi:10.1016/j.procs.2014.05.421.

    Article  Google Scholar 

  18. Chatterjee, M., Das, S., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Cluster Computing, 5(2), 193–204. doi:10.1023/A:1013941929408.

    Article  Google Scholar 

  19. Shah, M., Verma, P., Merchant, S., & Desai, U. (2011). Human walk aware mobility resistant efficient clustering for data gathering in cellphone based wireless sensor networks. In 20th Annual wireless and optical communications conference (WOCC) (pp. 1–6). Newark, USA.

  20. Singhal, S., & Daniel, A. K. (2014). Cluster head selection protocol under node degree, competence level and goodness factor for mobile ad hoc network using AI technique. In Proceedings of the 2014 fourth international conference on advanced computing & communication technologies (ACCT 14) (pp. 415–420). Rohtak, India.

  21. Basagni, S. (1999). Distributed and mobility-adaptive clustering for multimedia support in multi-hop wireless networks. In IEEE VTS 50th vehicular technology conference (VTC 1999) (pp 889–893 vol.2). Amsterdam, Netherland.

  22. Al-kahtani, M.S., & Mouftah, H.T. (2005) A stable clustering formation infrastructure protocol in mobile ad hoc networks. In IEEE International conference on wireless and mobile computing, networking and communications (WiMob’2005) (pp 406–413 Vol. 3). Montreal, Canada.

  23. Ding, R., Du, H., & Yang, B. (2011). Energy-based cluster-head inheritance algorithm for wireless sensor networks. In International conference on cyber-enabled distributed computing and knowledge discovery (CyberC 2011) (pp. 329–335). Beijing, China.

  24. Jemili, I., Belghith, A., & Mosbah, M. (2008). A synchronous tiered based clustering algorithm for large-scale ad hoc networks. In Z. Mammeri (Ed.), Wireless and mobile networking, IFIP international federation for information processing (Vol. 284, pp. 41–55). Berlin: Springer.

    Google Scholar 

  25. Bellavista, P., & Magistretti, E. (2007). K-hop backbone formation in ad hoc networks. In Proceedings of 16th international computer communications and networks (ICCCN 2007) (pp. 479–484). Hawaii, USA.

  26. Mai, K.T., & Choo, H. (2008). Connectivity-based clustering scheme for mobile ad hoc networks. In IEEE international conference on research, innovation and vision for the future (RIVF 2008) (pp 191–197). Ho Chi Minh City, Vietnam.

  27. Yu, J.Y., & Chong, P.H.J. (2003). 3hBAC (3-hop between adjacent clusterheads): a novel non-overlapping clustering algorithm for mobile ad hoc networks. In IEEE Pacific rim conference on communications, computers and signal processing (PACRIM 2003) (pp 318–321 vol.1). Victoria, Canada.

  28. Er, I.I., & Seah, W. (2004) Mobility-based d-hop clustering algorithm for mobile ad hoc networks. In IEEE wireless communications and networking conference (WCNC 2004) (pp 2359–2364 Vol.4). Atlanta, USA.

  29. Khan, A. U. R., Ali, S., Mustafa, S., & Othman, M. (2012). Impact of mobility models on clustering based routing protocols in mobile WSNs. In Proceedings of the 2012 10th international conference on Frontiers of information technology (FIT ’12) (pp. 366–370). Islamabad, Pakistan.

  30. Venkateswaran, A., Sarangan, V., Gautam, N., & Acharya, R. (2005). Impact of mobility predection on the temporal stability of MANET clustering algorithms. In Proceedings of the 2nd ACM international workshop on performance evaluation of wireless ad hoc, sensor, and ubiquitous Networks (pp. 144–151). Quebec, Canada.

  31. Su, W., Lee, S.J., & Gerla, M. (1) Mobility predection in wireless networks. In 21st century military communications conference proceedings (MILCOM 2000) (pp 491–495 vol.1). Los Angeles, USA.

  32. Zaidi, Z. R., Mark, B. L., & Thomas, R. K. (2004). A two-tier representation of node mobility in ad hoc networks. In First annual IEEE communications society conference on sensor and ad hoc communications and networks (SECON 2004) (pp. 153–161). Santa Clara, USA.

  33. Doci, A., Samakovitis, G., & Raju, V. (2009). Impact of mobility in ad hoc protocol design. In World congress on computer science and information engineering (WRI 2009) (pp. 38–43). Los Angeles, USA.

  34. Lenders, V., Wagner, J., & May, M. (2006). Analyzing the impact of mobility in ad hoc networks. In Proceedings of the 2nd international workshop on multi-hop ad hoc networks: From theory to reality (REALMAN ’06) (pp. 39–46). Florence, Italy.

  35. Maan, F., & Mazhar, N. (2011). MANET routing protocols vs mobility models: A performance evaluation. In Third international conference on ubiquitous and future networks (ICUFN 2011) (pp. 179–184). Dalian, China.

  36. Lee, K., Hong, S., Kim, S. J., Rhee, I., & Chong, S. (2012). SLAW: Self-similar least-action human walk. IEEE/ACM Transactions on Networking, 20(2), 515–529. doi:10.1109/TNET.2011.2172984.

    Article  Google Scholar 

  37. Chandra, S., & Bharti, A. K. (2013). Speed distribution curves for pedestrians during walking and crossing. Procedia–Social and Behavioral Sciences, 104, 660–667. doi:10.1016/j.sbspro.2013.11.160.

    Article  Google Scholar 

  38. González, M. C., Hidalgo, C. A., & Barabási, A. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. doi:10.1038/nature06958.

    Article  Google Scholar 

  39. Karagiannis, T., Le Boudec, J. Y., & Vojnovic, M. (2010). Power law and exponential decay of intercontact times between mobile devices. IEEE Transactions on Mobile Computing, 9(10), 1377–1390. doi:10.1109/TMC.2010.99.

    Article  Google Scholar 

  40. Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S. J., & Chong, S. (2011). On the levy-walk nature of human mobility. IEEE/ACM Transactions on Networking, 19(3), 630–643. doi:10.1109/INFOCOM.2008.145.

    Article  Google Scholar 

  41. Chiang, C. C., Liu, W., Wu, H. K., & Gerla, M. (1997). Routing in clustered multihop mobile wireless networks with fading channel. In IEEE Singapore international conference on networks (SICON’97) (pp. 197–211). Singapore, Singapore.

  42. Cavalcanti, E. R., & Spohn, M. A. (2011). Degree of node proximity: A spatial mobility metric for manets. In Proceedings of the 9th ACM international symposium on mobility management and wireless access (MobiWac 2011) (pp. 61–68). Florida, USA.

  43. Zhang, Y., & Ng, J. M. (2008). A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks. In IEEE international conference on communications (ICC 2008) (pp. 3161–3165). Beijing, China.

  44. INET (2016) INET framework. http://inet.omnetpp.org/. Accessed 2016-08-16.

  45. Varga, A., & Hornig, R. (2008). An overview of the OMNeT++ simulation environment. In Proceedings of the 1st international conference on simulation tools and techniques for communications, networks and systems & workshops (SIMUtools) (pp. 1–10). Marseille, France.

  46. Vajna, S., Tóth, B., & Kertész, J. (2013). Modelling bursty time series. New Journal of Physics 15(10), 103,023 http://stacks.iop.org/1367-2630/15/i=10/a=103023.

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Acknowledgments

The corresponding author was supported by a CONACyT scholarship (Mexico).

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Correspondence to Adán G. Medrano-Chávez.

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Medrano-Chávez, A.G., Pérez-Cortés, E. & Lopez-Guerrero, M. A Study on Cluster Stabilization Strategies for Human-Driven MANETs. Wireless Pers Commun 95, 795–817 (2017). https://doi.org/10.1007/s11277-016-3799-2

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