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A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks

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Abstract

A wireless sensor network (WSN) consists of a set of sensor nodes that are widely scattered in inaccessible areas. When deployed in large areas, WSNs generate a large volume of the data. Therefore, efficient methods are needed to process the data. One solution to minimize traffic on large-scale wireless sensor networks is to use data aggregation schemes. In this paper, a secure data aggregation method is proposed. The proposed secure data aggregation scheme has three phases: intra-cluster data aggregation, inter-cluster data aggregation, and data transfer. In the intra-cluster data aggregation phase, a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes. In the inter-cluster data aggregation phase, an aggregation tree is created between the cluster head nodes. The dragonfly algorithm (DA) is used to find the optimal aggregation tree between cluster head nodes. In the data transfer phase, the columnar transposition cipher method is used to establish a secure connection between cluster member nodes and their cluster head node. Also, a symmetric and lightweight encryption method based on the residue number system (RNS) is utilized to provide secure communications between the cluster head nodes. We modify RNS and call it RNS+. Finally, the simulation results of the proposed scheme are compared to three data aggregation methods including Sign-share, Sham-share, and RCDA. The results show that the proposed data aggregation scheme outperforms other data aggregation methods in terms of network lifetime, delay and packet delivery rate.

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References

  1. Radhappa H, Pan L, Xi Zheng J, Wen S (2018) Practical overview of security issues in wireless sensor network applications. Int J Comput Appl 40(4):202–213. https://doi.org/10.1080/1206212X.2017.1398214

    Google Scholar 

  2. Mostafaei H, Menth M (2018) Software-defined wireless sensor networks: A survey. J Netw Comput Appl 119:42–56. https://doi.org/10.1016/j.jnca.2018.06.016

    Article  Google Scholar 

  3. Jan MA, Khan F, Alam M (2019) Recent trends and advances in wireless and IoT-enabled networks. Springer, Berlin. https://doi.org/10.1007/978-3-319-99966-1

    Book  Google Scholar 

  4. Kim D-S, Tran-Dang H (2019) Wireless sensor networks for industrial applications. In: Industrial sensors and controls in communication networks. Springer, pp 127–140. https://doi.org/10.1007/978-3-030-04927-0_10

  5. Chokkareddy R, Thondavada N, Thakur S, Kanchi S (2019) Recent trends in sensors for health and agricultural applications. In: Advanced biosensors for health care applications. Elsevier, pp 341–355. https://doi.org/10.1016/B978-0-12-815743-5.00013-5

  6. Abdollahzadeh S, Navimipour NJ (2016) Deployment strategies in the wireless sensor network: A comprehensive review. Comput Commun 91:1–16. https://doi.org/10.1016/j.comcom.2016.06.003

    Article  Google Scholar 

  7. Yousefpoor MS, Barati H (2019) Dynamic key management algorithms in wireless sensor networks: A survey. Comput Commun 134:52–69. https://doi.org/10.1016/j.comcom.2018.11.005

    Article  Google Scholar 

  8. Boubiche S, Boubiche DE, Bilami A, Toral-Cruz H (2018) Big data challenges and data aggregation strategies in wireless sensor networks. IEEE Access 6:20558–20571. https://doi.org/10.1109/ACCESS.2018.2821445

    Article  Google Scholar 

  9. Liu Y, Peng H, Wu Y, Zeng J, Chen H, Wang K, Lai W, Li C (2018) Secure data aggregation with integrity verification in wireless sensor networks. In: International conference on database systems for advanced applications. Springer, pp 717–733. https://doi.org/10.1007/978-3-319-91452-7_46

  10. Kumar V, Madria S (2013) Pip: Privacy and integrity preserving data aggregation in wireless sensor networks. In: 2013 IEEE 32nd International symposium on reliable distributed systems. IEEE, pp 10–19. https://doi.org/10.1109/SRDS.2013.10

  11. Vinodha D, Anita EM (2019) Secure data aggregation techniques for wireless sensor networks: a review. Arch Comput Methods Eng 26(4):1007–1027. https://doi.org/10.1007/s11831-018-9267-2

    Article  Google Scholar 

  12. Randhawa S, Jain S (2017) Data aggregation in wireless sensor networks: Previous research, current status and future directions. Wirel Pers Commun 97(3):3355–3425. https://doi.org/10.1007/s11277-017-4674-5

    Article  Google Scholar 

  13. Jesus P, Baquero C, Almeida PS (2014) A survey of distributed data aggregation algorithms. IEEE Commun Surv Tutor 17(1):381–404. https://doi.org/10.1109/COMST.2014.2354398

    Article  Google Scholar 

  14. Seba A, Nouali-Taboudjemat N, Badache N, Seba H (2019) A review on security challenges of wireless communications in disaster emergency response and crisis management situations. J Netw Comput Appl 126:150–161. https://doi.org/10.1016/j.jnca.2018.11.010

    Article  Google Scholar 

  15. Chakraborty B, Verma S, Singh KP (2020) Temporal differential privacy in wireless sensor networks. J Netw Comput Appl 155:102548. https://doi.org/10.1016/j.jnca.2020.102548

    Article  Google Scholar 

  16. Di Pietro R, Guarino S, Verde NV, Domingo-Ferrer J (2014) Security in wireless ad-hoc networks–a survey. Comput Commun 51:1–20. https://doi.org/10.1016/j.comcom.2014.06.003

    Article  Google Scholar 

  17. Souissi I, Azzouna NB, Said LB (2019) A multi-level study of information trust models in wsn-assisted iot. Comput Netw 151:12–30. https://doi.org/10.1016/j.comnet.2019.01.010

    Article  Google Scholar 

  18. Bhushan B, Sahoo G (2018) Recent advances in attacks, technical challenges, vulnerabilities and their countermeasures in wireless sensor networks. Wirel Pers Commun 98(2):2037–2077. https://doi.org/10.1007/s11277-017-4962-0

    Article  Google Scholar 

  19. Merad Boudia OR, Senouci SM, Feham M (2018) Secure and efficient verification for data aggregation in wireless sensor networks. Int J Netw Manag 28(1):e2000. https://doi.org/10.1002/nem.2000

    Article  Google Scholar 

  20. Zhang P, Wang J, Guo K, Wu F, Min G (2018) Multi-functional secure data aggregation schemes for wsns. Ad Hoc Netw 69:86–99. https://doi.org/10.1016/j.adhoc.2017.11.004

    Article  Google Scholar 

  21. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  MathSciNet  Google Scholar 

  22. Lasry G, Kopal N, Wacker A (2016) Cryptanalysis of columnar transposition cipher with long keys. Cryptologia 40(4):374–398. https://doi.org/10.1080/01611194.2015.1087074

    Article  Google Scholar 

  23. Ye R, Boukerche A, Wang H, Zhou X, Yan B (2018) Resident: a reliable residue number system-based data transmission mechanism for wireless sensor networks. Wirel Netw 24(2):597–610. https://doi.org/10.1007/s11276-016-1357-1

    Article  Google Scholar 

  24. Boubiche DE, Boubiche S, Toral-Cruz H, Pathan A-SK, Bilami A, Athmani S (2016) Sdaw: secure data aggregation watermarking-based scheme in homogeneous wsns. Telecommun Syst 62(2):277–288. https://doi.org/10.1007/s11235-015-0047-0

    Article  Google Scholar 

  25. Zhong H, Shao L, Cui J, Xu Y (2018) An efficient and secure recoverable data aggregation scheme for heterogeneous wireless sensor networks. J Parallel Distrib Comput 111:1–12. https://doi.org/10.1016/j.jpdc.2017.06.019

    Article  Google Scholar 

  26. Alghamdi WY, Wu H, Kanhere SS (2017) Reliable and secure end-to-end data aggregation using secret sharing in wsns. In: 2017 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1–6, DOI https://doi.org/10.1109/WCNC.2017.7925558, (to appear in print)

  27. Chen C-M, Lin Y-H, Lin Y-C, Sun H-M (2011) Rcda: Recoverable concealed data aggregation for data integrity in wireless sensor networks. IEEE Trans Parallel Distrib Syst 23(4):727–734. 10.1109/TPDS.2011.219

    Article  Google Scholar 

  28. Hua P, Liu X, Yu J, Dang N, Zhang X (2018) Energy-efficient adaptive slicebased secure data aggregation scheme in wsn. Procedia Comput Sci 129:188–193. https://doi.org/10.1016/j.procs.2018.03.033

    Article  Google Scholar 

  29. Ullah A, Said G, Sher M, Ning H (2020) Fog-assisted secure healthcare data aggregation scheme in iot-enabled wsn. Peer Peer Netw Appl 13(1):163–174. https://doi.org/10.1007/s12083-019-00745-z

    Article  Google Scholar 

  30. Wang X, Zhou Q, Cheng C-T (2019) A uav-assisted topology-aware data aggregation protocol in wsn. Phys Commun 34:48–57. https://doi.org/10.1016/j.phycom.2019.01.012

    Article  Google Scholar 

  31. Siddique N, Adeli H (2013) Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. Wiley, New York

    Book  Google Scholar 

  32. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  33. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

  34. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

  35. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Article  Google Scholar 

  36. Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, Berlin, pp 169–178

  37. Yousefpoor MS, Barati H (2019) Dskms: a dynamic smart key management system based on fuzzy logic in wireless sensor networks. Wirel Netw :1–21

  38. Issariyakul T, Hossain E (2009) Introduction to network simulator 2 (ns2). In: Introduction to network simulator NS2. Springer, pp 1–18. https://doi.org/10.1007/978-0-387-71760-9_2

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Correspondence to Efat Yousefpoor.

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Yousefpoor, E., Barati, H. & Barati, A. A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks. Peer-to-Peer Netw. Appl. 14, 1917–1942 (2021). https://doi.org/10.1007/s12083-021-01116-3

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