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Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks

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Abstract

Network lifetime is the key design parameter for wireless sensor network protocols. In recent years, based on energy efficient routing techniques numerous methods have been proposed for enhancing network lifetime. These methods have mainly considered residual energy, number of hops and communication cost as route selection metrics. This paper introduces a method for further improvement in the network lifetime by considering network connectivity along with energy efficiency for the selection of data transmission routes. The network lifetime is enhanced by preserving highly connected nodes at initial rounds of data communication to ensure network connectivity during later rounds. Bassed on the above mentioned concept, a connectivity aware routing algorithm: CARA has been proposed. In the proposed algorithm, connectivity factor of a node is calculated on the basis of Betweenness centrality of a node and energy efficient routes are found by using fuzzy logic and ant colony optimization. The simulation results show that the proposed algorithm CARA performs better than other related state-of-the-art energy efficient routing algorithms viz. FML, EEABR and FACOR in terms of network lifetime, connectivity, energy dissipation, load balancing and packet delivery ratio.

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Notes

  1. In unicast routing, each forwarding node select one of its neighbouring nodes as its next-hop relaying node.

  2. In future, the proposed routing method can be extended in the field of sleep awake scheduling with cross layer interaction of network layer and MAC layer.

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  2. Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D. Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions onMobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  3. Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.

    Article  Google Scholar 

  4. Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  5. Manap, Z., Ali, B. M., Ng, C. K., Noordin, N. K., & Sali, A. (2013). A review on hierarchical routing protocols for wireless sensor networks. Wireless Personal Communications, 72(2), 1077–1104.

    Article  Google Scholar 

  6. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the IETF protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98.

    Article  Google Scholar 

  7. Vasilakos, A. V., Zhang, Y., & Spyropoulos, T. (Eds.). (2011). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  8. Chen, M., Wan, J., González, S., Liao, X., & Leung, V. (2014). A survey of recent developments in home M2M networks. IEEE Communications Surveys and Tutorials, 16(1), 98–114.

    Article  Google Scholar 

  9. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  10. Shen, Z., Luo, J., Zimmermann, R., & Vasilakos, A. V. (2011). Peer-to-peer media streaming: Insights and new developments. Proceedings of the IEEE, 99(12), 2089–2109.

    Article  Google Scholar 

  11. Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  12. Wasserman, S. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  13. Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.

    Article  Google Scholar 

  14. Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170–1182.

    Article  Google Scholar 

  15. Aarti J., Reddy, B. V. R. (2013). Node centrality in wireless sensor networks: Importance, applications and advances. In Proceedings of 3rd IEEE international advanced computing conference (IACC-2013) (pp. 126–130).

  16. Marwaha, S., Srinivasan, D., Tham, C. K., & Vasilakos, A. (2004). Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks. In Congress on evolutionary computation, 2004. CEC2004 (Vol. 2, pp. 1964–1971).

  17. Koczy, L. T. (1992). Fuzzy graphs in the evaluation and optimization of networks. Fuzzy Sets and Systems, 46, 307–319.

    Article  MathSciNet  MATH  Google Scholar 

  18. Dorigo, M. (Ed.). (2006). Ant colony optimization and swarm intelligence. In Proceedings of 5th international workshop, ANTS 2006 (Vol. 4150), Brussels, Belgium, September 4–7, 2006. Springer.

  19. Lin, Ying, et al. (2012). An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(3), 408–420.

    Article  Google Scholar 

  20. Chandra Mohan, B., & Baskaran, R. (2012). A survey: Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 39(4), 4618–4627.

    Article  Google Scholar 

  21. Chang, J. H., & Tassiulas, L. (2004). Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 12(4), 609–619.

    Article  Google Scholar 

  22. Akkaya, K., & Younis, M. (2003). An energy-aware QoS routing protocol for wireless sensor networks. In Proceedings of 23rd IEEE international conference on distributed computing systems workshops, 2003.

  23. Akkaya, Kemal, & Younis, Mohamed. (2004). Energy-aware delay-constrained routing in wireless sensor networks. International Journal of Communication Systems, 17(6), 663–687.

    Article  Google Scholar 

  24. Heo, J., Jiman, H., & Yookun, C. (2009). EARQ: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 5(1), 3–11.

    Article  Google Scholar 

  25. Li, W., Chen, M., & Li, M. M. (2009). An enhanced aodv route protocol applying in the wireless sensor networks. In Fuzzy information and engineering (Vol. 2, pp. 1591–1600). Berlin: Springer.

  26. Perkins, C. E., & Royer, E. M. (1999). Ad hoc on-demand distance vector routing. In Proceedings of the second IEEE workshop on mobile computing systems and applications, 1999, WMCSA’99 (pp. 90–100).

  27. Park, Joongseok, & Sahni, Sartaj. (2006). An online heuristic for maximum lifetime routing in wireless sensor networks. IEEE Transactions on Computers, 55(8), 1048–1056.

    Article  Google Scholar 

  28. Kar, K., Kodialam, M., Lakshman, T., & Tassiulas, L. (2003). Routing for network capacity maximization in energy-constrained ad-hoc networks. In Proceedings of IEEE INFOCOM, 2003.

  29. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors. doi:10.1155/2009/134165.

    Google Scholar 

  30. Dvir, A., & Vasilakos, A. V. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    Google Scholar 

  31. Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.

    Google Scholar 

  32. Minhas, M. R., Gopalakrishnan, S., & Leung, V. C. M. (2008). Fuzzy algorithms for maximum lifetime routing in wireless sensor networks. In Global telecommunications conference, 2008. IEEE GLOBECOM 2008.

  33. Jabbar, S., Minhas, A. A., Akhtar, R. A., & Aziz, M. Z. (2009). REAR: Real-time energy aware routing for wireless adhoc micro sensors network. In Eighth IEEE international conference on dependable, autonomic and secure computing, 2009. DASC’09 (pp. 825–830).

  34. AlShawi, I. S., Yan, L., Pan, W., & Luo, B. (2012). Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. IEEE Sensors Journal, 12(10), 3010–3018.

    Article  Google Scholar 

  35. ALMomani, I. M., & Saadeh, M. K. (2011). FEAR: Fuzzy-based energy aware routing protocol for wireless sensor networks. International Journal of Communications, Network and System Sciences, 4(06), 403.

    Article  Google Scholar 

  36. Cheng, H., Xiong, N., Vasilakos, A. V., Yang, L. T., Chen, G., & Zhuang, X. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.

    Article  Google Scholar 

  37. Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1093–1102.

    Article  Google Scholar 

  38. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  39. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  40. Yen, Y. S., Chao, H. C., Chang, R. S., & Vasilakos, A. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11), 2238–2250.

    Article  Google Scholar 

  41. Kassotakis, I. E., Markaki, M. E., & Vasilakos, A. V. (2000). A hybrid genetic approach for channel reuse in multiple access telecommunication networks. IEEE Journal on Selected Areas in Communications, 18(2), 234–243.

    Article  Google Scholar 

  42. Vasilakos, A., Saltouros, M. P., Atlassis, A. F., & Pedrycz, W. (2003). Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 33(3), 297–312.

    Article  Google Scholar 

  43. Camilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In Ant colony optimization and swarm intelligence (pp. 49–59). Berlin: Springer.

  44. Ding, N., & Liu, P. X. (2005). A centralized approach to energy-efficient protocols for wireless sensor networks. In 2005 IEEE international conference mechatronics and automation (Vol. 3, pp. 1636–1641).

  45. Wen, Y. F., Chen, Y. Q., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using Energy* Delay metrics. Journal of Zhejiang University Science A, 9(4), 531–538.

    Article  MATH  Google Scholar 

  46. Liu, Y., Zhu, H., Xu, K., & Jia, Y. (2007). A routing strategy based on ant algorithm for WSN. In Third international conference on natural computation, 2007. ICNC 2007 (Vol. 5, pp. 685–689).

  47. GhasemAghaei, Reza, et al. (2007). Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks. In Instrumentation and measurement technology conference proceedings. IMTC 2007.

  48. Xiu-li, Ren, Liang Hong-wei, and Wang Yu. (2008). Multipath routing based on ant colony system in wireless sensor networks. In 2008 International conference on computer science and software engineering (Vol. 3).

  49. Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In 2013 IEEE 10th international conference on Mobile ad-hoc and sensor systems (MASS) (pp. 182–190).

  50. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. In IEEE transactions on networking (Vol. 23, No. 3).

  51. Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON) (pp. 46–54).

  52. Liu, X. Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., & Wu, M. Y. (2014). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. doi:10.1109/TPDS.2014.2345257.

    Google Scholar 

  53. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.

    Article  Google Scholar 

  54. Amiri, E., Keshavarz, H., Alizadeh, M., Zamani, M., & Khodadadi, T. (2014). Energy efficient routing in wireless sensor networks based on fuzzy ant colony optimization. International Journal of Distributed Sensor Networks. doi:10.1155/2014/768936.

    Google Scholar 

  55. Zhang, Q. Y., Sun, Z. M., & Zhang, F. (2014). A clustering routing protocol for wireless sensor networks based on type-2 fuzzy logic and ACO. In 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1060–1067).

  56. Gajjar, S., Sarkar, M., & Dasgupta, K. (2015). FAMACRO: Fuzzy and ant colony optimization based MAC/routing cross-layer protocol for wireless sensor networks. Procedia Computer Science, 46, 1014–1021.

    Article  Google Scholar 

  57. Tomar, G. S., Sharma, T., & Kumar, B. (2015). Fuzzy based ant colony optimization approach for wireless sensor network. Wireless Personal Communications, 84(1), 361–375.

    Article  Google Scholar 

  58. Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2008). Hybrid approach using ant colony optimization and fuzzy logic to solve multi-criteria hybrid flow shop scheduling problem. In Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology (ACM) (pp. 44–50).

  59. Van Ast, J., Babuska, R., & De Schutter, B. (2009). Fuzzy ant colony optimization for optimal control. In American control conference, 2009. ACC’09 (pp. 1003–1008).

  60. Alsawy, A. A., & Hefny, H. A. (2010). Fuzzy-based ant colony optimization algorithm. In 2nd international conference on computer technology and development (ICCTD), 2010 (pp. 530–534).

  61. Ginidi, A. R. G., Kamel, A. M., & Dorrah, H. T. (2010). Development of new fuzzy logic-based ant colony optimization algorithm for combinatorial problems. In Proceedings of the 14th international middle east power systems conference, Cairo University, Egypt.

  62. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  63. Ross, T. (2004). Fuzzy logic with engineering applications (2nd ed.). Chichester: Wiley.

    MATH  Google Scholar 

  64. Baglioni, M., Geraci, F., Pellegrini, M., & Lastres, E. (2012). Fast exact computation of betweenness centrality in social networks. In Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012) (IEEE Computer Society) (pp. 450–456).

  65. Brandes, U., & Pich, C. (2007). Centrality estimation in large networks. International Journal of Bifurcation and Chaos, 17(07), 2303–2318.

    Article  MathSciNet  MATH  Google Scholar 

  66. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25, 163–177.

    Article  MATH  Google Scholar 

  67. Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136–145.

    Article  MathSciNet  Google Scholar 

  68. Jain, A., & Reddy, B. R. (2015). A Novel method of modeling wireless sensor network using fuzzy graph and energy efficient fuzzy based k-hop clustering algorithm. Wireless Personal Communications, 82(1), 157–181.

    Article  Google Scholar 

  69. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1, 660–670.

    Article  Google Scholar 

  70. Narayanaswamy, S., Kawadia, V., Sreenivas, R. S., & Kumar, P. (2002). Power control in ad-hoc networks: Theory, architecture, algorithm and implementation of the COMPOW protocol. In European wireless conference.

  71. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of international workshop mobile wireless communication networks (pp. 368–372).

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Jain, A. Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks. Wireless Netw 22, 1605–1624 (2016). https://doi.org/10.1007/s11276-015-1054-5

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