Skip to main content

Advertisement

Log in

A Cross Layer Design and Flower Pollination Optimization Algorithm for Secured Energy Efficient Framework in Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

One of the fast-growing, tremendous and highly demanding networks from various emerging applications like military, healthcare industry, airport and forests is wireless sensor network. These applications are incorporated into the cloud in order to utilize various energy resources, high performance computing and massive storage structure. Many challenges should be resolved in sensor cloud environment like security, continuous data transmission, power management and energy efficiency. Some of the earlier research works anticipated data compression method for energy conservation and encryption decryption algorithms for secured data transmission. Compressing different data from various sources is difficult which degrades the performance of the data transmission and encryption decryption has taken more computational time and complexity which raises the need for energy efficiently and secured data transmission schemes. Hence, this paper is motivated to solve the major issues in sensor cloud, energy efficiency, data security and maintenance and thereby improving the quality of service (QoS). A secured energy efficient framework (SEEF) is designed for secured data transmission and maintenance which comprises of various phases like a cross layer designed for power and traffic management, node and route investigation for security and dynamic optimal routing for improving the QoS using flower pollination algorithm. A smart duty cycle scheduling protocol (SDCS) is proposed to increase the lifetime of the network by avoiding data redundancy, transmission conflicts, traffic conflicts and energy reduction in the network. The end to end delay is reduced by reducing the interval between the packets and packet delivery ratio is improved by avoiding packet loss. SEEF is simulated in NS2 simulator and the results are verified. The performance shows that the throughput and packet delivery ratio is improved up to 0.57 MB/s and 96% respectively. The end to end delay and the energy consumed is reduced to 0.69 s and 0.076 J respectively. From the comparison it is identified that SEEF can increase the overall performance in sensor cloud.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A. H., & Hossain, M. A. (2012). A survey on sensor-cloud: Architecture, applications, and approaches. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2013/917923.

    Article  Google Scholar 

  2. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks,67, 104–122. https://doi.org/10.1016/j.comnet.2014.03.027.

    Article  Google Scholar 

  3. Yang, Q., Zhu, X., Fu, H., & Che, X. (2014). Survey of security technologies on wireless sensor networks. Journal of Sensors. https://doi.org/10.1155/2015/842392.

    Article  Google Scholar 

  4. Sen, J. (2013). Security and privacy challenges in cognitive wireless sensor networks. In Cognitive radio technology applications for wireless and mobile Ad hoc networks (pp. 194–232). IGI Global, USA. https://doi.org/10.4018/978-1-4666-4221-8.ch011.

    Google Scholar 

  5. Al-Anbagi, I., Erol-Kantarci, M., & Mouftah, H. T. (2016). A survey on cross-layer quality-of-service approaches in WSNs for delay and reliability-aware applications. IEEE Communication Surveys and Tutorials,18(1), 525–552. https://doi.org/10.1109/COMST.2014.2363950.

    Article  Google Scholar 

  6. Han, G., Jiang, J., Shu, L., Niu, J., & Chao, H. C. (2013). Management and applications of trust in wireless sensor networks: A survey. Journal of Computer and System Sciences,80(3), 602–617. https://doi.org/10.1016/j.jcss.2013.06.014.

    Article  MATH  Google Scholar 

  7. Oliveira, L. M., De Sousa, A. F., & Rodrigues, J. J. (2011). Routing and mobility approaches in iPv6 over lowpan mesh networks. International Journal of Communication System,4(11), 1445–1466. https://doi.org/10.1002/dac.1228.

    Article  Google Scholar 

  8. Rodrigues, J. J., & Neves, P. A. (2010). A survey on IP-based wireless sensor network solutions. International Journal of Communication Systems,23(8), 963–981. https://doi.org/10.1002/dac.1099.

    Article  Google Scholar 

  9. Verma, A., Singh, M. P., Singh, J. P., & Kumar, P. (2015). Survey of MAC protocol for wireless sensor networks. In Proceedings of the 2nd international conference on advances in computing and communication engineering (ICACCE), Dehradun, India. https://doi.org/10.1109/icacce.2015.29.

  10. Al-Anbagi, I., Erol-Kantarci, M., & Mouftah, H. (2014). Priority and delay-aware medium access for wireless sensor networks in the smart grid. IEEE System Journal,8(2), 608–618. https://doi.org/10.1109/JSYST.2013.2260939.

    Article  Google Scholar 

  11. Kim, T., Love, D. J., Skoglund, M., & Jin, Z.-Y. (2015). An approach to sensor network throughput enhancement by phy-aided MAC. IEEE Transactions on Wireless Communications,14(3), 670–684. https://doi.org/10.1109/TWC.2014.2356507.

    Article  Google Scholar 

  12. Wang, G., Yu, J., Yu, D., Yu, H., & Feng, L. (2015). DS-MAC: An energy efficient demand sleep MAC protocol with low latency for wireless sensor networks. Journal of Network and Computer Applications,58, 155–164. https://doi.org/10.1016/j.jnca.2015.09.007.

    Article  Google Scholar 

  13. Saraereh, O. A., Khan, I., & Lee, B. M. (2018). An efficient neighbor discovery scheme for mobile WSN. IEEE Access, 7, 4843–4855. https://doi.org/10.1109/ACCESS.2018.2886779.

    Article  Google Scholar 

  14. Wan, R., Xiong, N., & Nguyen The Loc. (2018). An energy—Efficient sleep scheduling mechanism with similarity for wireless sensor networks. Berlin: Springer.

    Book  Google Scholar 

  15. El-Sayed, W. M., El-Bakry, H. M., & El-Sayed, S. M. (2019). Integrated data reduction model in wireless sensor networks. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2019.03.003.

    Article  MATH  Google Scholar 

  16. Feng, J., & Zhao, H. (2018). Energy-balanced multisensory scheduling for target tracking in wireless sensor networks. Sensor,18, 3585.

    Article  Google Scholar 

  17. Ali, I., Ang, T. F., Khan, N., Hussain, M. R., Por, L. Y., Adam, M. S., et al. (2019). An adaptive wake-up-interval to enhance receiver-based Ps-Mac protocol for wireless sensor networks. Sensor,19, 3732.

    Article  Google Scholar 

  18. Wang, L., Yan, J., Han, T., & Deng, D. (2019). On connectivity and energy efficiency for sleeping-scheduling-based wireless sensor networks. Sensor,19, 2126.

    Article  Google Scholar 

  19. Banimelhem, O., Mowafi, M., & Aljoby, W. (2013). Genetic algorithm-based deployment in hybrid wireless sensor networks. Communications and Network,5(4), 273–279. https://doi.org/10.4236/cn.2013.54034.

    Article  Google Scholar 

  20. Senguptaa, S., Dasb, S., Nasira, M. D., & Panigrahic, B. K. (2013). Multi-objective node deployment in WSNs: In search of an optimal trade of among coverage, lifetime, energy consumption, and connectivity. Engineering Applications of Artificial Intelligence,26(1), 405–416. https://doi.org/10.1016/j.engappai.2012.05.018.

    Article  Google Scholar 

  21. Qin, X., & Berry, R. (2003). Exploiting multiuser diversity for medium access control in wireless networks. In IEEE INFOCOM 2003. 22nd annual joint conference of the IEEE computer and communications societies (IEEE Cat. No.03CH37428), San Francisco, CA, USA.

  22. Omori, K., Tanigawa, Y., Tode, H. (2015). A study on power saving using RTS/CTS handshake and burst transmission in wireless LAN. In Proceedings of 10th Asia-Pacific symposium on information and telecommunication technologies (APSITT), Colombo, Sri Lanka. https://doi.org/10.1109/apsitt.2015.7217123.

  23. Abdel-Basset, M., & Shawky, L. A. (2018). Flower pollination algorithm: A comprehensive review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-018-9624-4.

    Article  Google Scholar 

  24. Yang, X.-S. (2012). Flower pollination algorithm for global optimization. In Proceedings of the international conference on unconventional computation and natural computation (UCNC), Springer, Berlin. https://doi.org/10.1007/978-3-642-32894-7_27.

    Chapter  Google Scholar 

  25. JieTian, Y. W., Liang, X., Wang, G., & Zhang, Y. (2017). WA-MAC: A weather adaptive mac protocol in survivability-heterogeneous wireless sensor networks. Ad Hoc Networks,67, 40–52. https://doi.org/10.1016/j.adhoc.2017.10.005.

    Article  Google Scholar 

  26. Sun, Y., Gurewitz, O., & Johnson, D. B. (2008). RI-MAC: A receiver-initiated asynchronous duty cycle mac protocol for dynamic traffic loads in wireless sensor networks. In proceedings of the 6th ACM conference on embedded network sensor systems, (In: SenSys’08), Raleigh, NC, USA. https://doi.org/10.1145/1460412.1460414.

  27. Tang, L., Sun, Y., Gurewitz, O., & Johnson, D. B. (2011). PW-MAC: An energy-efficient predictive-wakeup MAC protocol for wireless sensor networks. In Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM 2011). https://doi.org/10.1109/infcom.2011.5934913.

  28. Peng, Y., Li, Z., Qiao, D., Zhang, W. (2011). Delay-bounded MAC with minimal idle listening for sensor networks. In 2011 Proceedings IEEE INFOCOM, Shanghai, China.

  29. Benothman, J., & Yahya, B. (2010). Energy efficient and QoS based routing protocol for wireless sensor networks. Journal of Parallel and Distributed Computing,70(8), 849–857. https://doi.org/10.1016/j.jpdc.2010.02.010.

    Article  MATH  Google Scholar 

  30. Weng, C. C., Chen, C. W., Chen, P. Y., & Chang, K. C. (2013). Design of an energy-efficient cross-layer protocol for mobile adhoc networks. IET Communications,7(3), 217–228. https://doi.org/10.1049/iet-com.2011.0543.

    Article  Google Scholar 

  31. Yang, X., Wang, L., & Xie, J. (2017). Energy efficient cross-layer transmission model for mobile wireless sensor networks. Mobile Information Systems. https://doi.org/10.1155/2017/1346416.

    Article  Google Scholar 

  32. Jiang, A., & Zheng, L. (2018). An effective hybrid routing algorithm in WSN: Ant colony optimization in combination with hop count minimization. Sensors,18(1020), 1–17. https://doi.org/10.3390/s18041020.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Subashini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subashini, S., Mathiyalagan, P. A Cross Layer Design and Flower Pollination Optimization Algorithm for Secured Energy Efficient Framework in Wireless Sensor Network. Wireless Pers Commun 112, 1601–1628 (2020). https://doi.org/10.1007/s11277-020-07118-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07118-3

Keywords

Navigation