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Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network

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Abstract

Due to the promising application of collecting information from remote or inaccessible location, wireless sensor networks pose big challenge for data routing to maximize the communication with more energy efficient. Literature presents different cluster-based energy aware routing protocol for maximizing the life time of sensor nodes. Accordingly, an energy efficient clustering mechanism, based on artificial bee colony algorithm and factional calculus is proposed in this paper to maximize the network energy and life time of nodes by optimally selecting cluster-head. The hybrid optimization algorithm called, multi-objective fractional artificial bee colony is developed to control the convergence rate of ABC with the newly designed fitness function which considered three objectives like, energy consumption, distance travelled and delays to minimize the overall objective. The performance of the proposed FABC-based cluster head selection is compared with LEACH, PSO and ABC-based routing using life time, and energy. The results proved that the proposed FABC maximizes the energy as well as life time of nodes as compared with existing protocols.

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References

  1. Gautam, N., & Pyun, J. Y. (2010). Distance aware intelligent clustering protocol for wireless sensor networks. Journal of communications and networks, 12(2), 122–129.

    Article  Google Scholar 

  2. Hammoudeh, M., & Newman, R. (2015). Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion, 22, 3–15.

    Article  Google Scholar 

  3. Lee, J. S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12, 2891–2897.

    Article  Google Scholar 

  4. Amgoth, T., & Jana, P. K. (2014). Energy-aware routing algorithm for wireless sensor networks. Computers and Electrical Engineering, 41, 357–367.

    Article  Google Scholar 

  5. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd international conference on system science (HICSS’00), Hawaii, USA (pp. 1–10).

  6. Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-centric Computing and Information Sciences. doi:10.1186/2192-1962-2-13.

  7. Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12, 1950–1957.

    Article  Google Scholar 

  8. Hoang, D. C., Yadav, P., Kumar, R., & Panda, S. K. (2014). Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1), 774–783.

    Article  Google Scholar 

  9. Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Network, 18, 847–860.

    Article  Google Scholar 

  10. Chen, R., Chang, W., Shieh, C., & Zou, C. C. (2012). Using hybrid artificial bee colony algorithm to extend wireless sensor network lifetime. In Proceedings of third international conference on innovations in bio-inspired computing and applications (156–161).

  11. Tan, Y. K., & Panda, S. K. (2011). Energy harvesting from hybrid indoor ambient light and thermal energy sources for enhanced performance of wireless sensor nodes. IEEE Transactions on Industrial Electronics, 58, 4424–4435.

    Article  Google Scholar 

  12. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3, 660–669.

    Article  Google Scholar 

  13. Tan, Y. K., & Panda, S. K. (2011). Self-autonomous wireless sensor nodes with wind energy harvesting for remote sensing of wind-driven wildfire spread. IEEE Transactions on Instrumentation and Measurement, 60, 1367–1377.

    Article  Google Scholar 

  14. Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. J. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7, 16–27.

    Article  Google Scholar 

  15. Tan, Y. K., & Panda, S. K. (2011). Optimized wind energy harvesting system using resistance emulator and active rectifier for wireless sensor nodes. IEEE Transactions on Power Electronics, 26, 38–50.

    Article  Google Scholar 

  16. Zhang, B., Simon, R., & Aydin, H. (2013). Harvesting-aware energy management for time-critical wireless sensor networks with joint voltage and modulation scaling. IEEE Transactions on Industrial Informations, 9, 514–526.

    Article  Google Scholar 

  17. Yu, M., Kin, K. L., & Ankit, M. (2007). A dynamic clustering and energy efficient routing techniques for sensor networks. IEEE Transactions on Wireless Communications, 6, 3069–3079.

    Article  Google Scholar 

  18. Luo, R. C., & Chen, O. (2012). Mobile sensor node deployment and asynchronous power management for wireless sensor networks. IEEE Transactions on Industrial Electronics, 59, 2377–2385.

    Article  MathSciNet  Google Scholar 

  19. Ren, F., Zhang, J., He, T., Lin, C., & Das, S. K. (2011). EBRA: energy-balanced routing protocol for data gathering in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22, 2018–2125.

    Google Scholar 

  20. Salam, A. H. S., & Olariu, S. (2012). BEES: Bioinspired backbone selection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23, 44–51.

    Article  Google Scholar 

  21. Jiguo, Y., Yingying, Q., Guangui, W., & Xin, G. (2012). A cluster-based routing protocol for wireless sensor with non-uniform node distribution. International Journal of Electronics and Communications, 66, 54–61.

    Article  Google Scholar 

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

  23. Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. IEEE/ACM Transactions on Networking, 23, 182–190.

    Google Scholar 

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

    Google Scholar 

  25. 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 

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

    Article  Google Scholar 

  27. 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. Wireless Communications, IEEE, 20, 91–98.

    Article  Google Scholar 

  28. Zenga, Y., Lia, D., & Vasilako, A. V. (2013). Real-time data report and task execution in wireless sensor and actuator networks using self-aware mobile actuators. Computer Communications, 36, 988–997.

    Article  Google Scholar 

  29. He, D., Chen, C., Chan, S., Bu, J., & Vasilakos, A. V. (2012). ReTrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16, 623–632.

    Article  Google Scholar 

  30. He, D., Chen, C., Chan, S., Bu, J., & Vasilakos, A. V. (2012). A distributed trust evaluation model and its application scenarios for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16, 1164–1175.

    Article  Google Scholar 

  31. Liu, J., Wang, Q., Wan, J., Xiong, J., & Zeng, Bi. (2013). Towards key issues of disaster aid based on wireless body area networks. KSII Transactions on Internet and Information Systems, 7, 1014–1035.

    Article  Google Scholar 

  32. Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. In Proceedings of the IEEE (Vol. 101, pp. 2470–2494).

  33. Vasilakos, A. V., Zhang, Y., & Spyropoulos, T. (2012). Delay tolerant networks: Protocols and applications. Boca Raton, FL: CRC Press.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Xiang, L., Luo J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In SECON (pp. 46–54).

  37. 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.

  38. Liu, J., Wan, J., Wang, Q., Deng, P., Zhou, K., & Qiao, Y. (2015). A survey on position-based routing for vehicular ad hoc networks. Springer Telecommunication Systems. doi:10.1007/s11235-015-9979-7.

    Google Scholar 

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

    Article  Google Scholar 

  40. 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, 42, 1093–1102.

    Article  Google Scholar 

  41. Weia, G., Linga, Y., Guoa, B., Xiaob, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34, 793–802.

    Article  Google Scholar 

  42. Chen, M., Gonzalez, S., Vasilakos, A. V., Cao, H., & Leung, V. C. (2011). Body area networks: A survey. MONET, 16, 171–193.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  45. 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, 417–430.

    Article  Google Scholar 

  46. Liu, L., Song, Y., Zhang, H., Ma, H., & Vasilakos, A. V. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64, 819–832.

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  48. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in SNs. ACM Transactions on Sensor Networks (TOSN). doi:10.1145/2700264.

  49. Li, P. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25, 3264–3273.

    Article  Google Scholar 

  50. 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 

  51. Ganesh, S., & Amutha, R. (2013). Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms. Journal of Communications and Networks, 15(4), 422–429.

    Article  Google Scholar 

  52. Yan, F., Yeung, A. K. H., Joseph, A. C., & Chen, G. (2015). Degree-energy-based local random routing strategies for sensor networks. Communications in Nonlinear Science and Numerical Simulation, 20, 250–262.

    Article  Google Scholar 

  53. Han, Z., Wu, J., Zhang, J., Liu, L., & Tian, K. (2014). A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Transactions on Nuclear Science, 61(2), 732–740.

    Article  Google Scholar 

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

    Article  Google Scholar 

  55. Pires, E. J. S., Machado, J. A. T., Oliveira, P. B. M., Cunha, J. B., & Mendes, L. (2010). Particle swarm optimization with fractional-order velocity. Nonlinear Dynamics, 61, 295–301.

    Article  MATH  Google Scholar 

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Correspondence to Rajeev Kumar.

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Kumar, R., Kumar, D. Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Netw 22, 1461–1474 (2016). https://doi.org/10.1007/s11276-015-1039-4

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