An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

  • M. ShobanaEmail author
  • R. Sabitha
  • S. Karthik
Methodologies and Application


Rapid growth in wireless technologies and communication, wireless sensor network (WSN) skills, data gathering and management models has paved the sensor technology a great impact on all factors of human life. In WSN, maximum consumption of constrained resources is considered to be the major challenge. Additionally, secure data aggregation has made the research domain more interesting. For consuming the limited sensor node resources optimally, data aggregation model plays a vital role. It reduces the redundant and unwanted data transmission and enhances the accuracy of data, thereby reducing the energy consumption rate and consumption overhead. Hence, for balancing the energy efficient data processing with secure data aggregation in large-scale WSN, optimized security model using enhanced fully homomorphic encryption (OSM-EFHE) has been developed in this work. First, the network is divided into clusters and cluster head which acts as an aggregator is selected based on the fuzzy if–then rule which helps in consumption of energy. Second, it provides data confidentiality and maintains subjective aggregation functions through fully homomorphic encryption (FHE). In this work, Van Dijk, Gentry, Halevi and Vaikunathan key generation plan with public key compression is used which condenses the public key dimension which is one of the major computations overhead for FHE. Finally, data integrity operation has also been induced with message authentication code. When comparing with the existing approaches, simulation results make a clear note of average delay of the network as 1.2 ms and a higher throughput of 4500 bps approximately. Thus, the overall transmission of data has been increased by means of employing OSM-EFHE model.


Security model using enhanced fully homomorphic encryption (OSM-EFHE) Data aggregation Message authentication code (MAC) DGHV key generation scheme Fuzzy logic Soft computing 



This research is not supported under any funding.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

All referred studies are highlighted in the literature review.


  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40:102–114CrossRefGoogle Scholar
  2. Albath J, Madria S (2009) Secure hierarchical data aggregation in wireless sensor networks. In: IEEE communications Society subject matter experts for publication in the WCNC 2009 proceedingsGoogle Scholar
  3. Bagci H, Yazici A (2013) An Energy Aware fuzzy approach to unequal clustering in wireless sensor networks. Elsevier, Amsterdam, pp 1741–1749Google Scholar
  4. Bista R, Jo KJ, Chang JW (2009) A new approach to secure aggregation of private data in wireless sensor networks. In: 2009 eighth IEEE international conference on dependable, autonomic and secure computingGoogle Scholar
  5. Boubiche DE, Boubiche S, Toral-Cruz H, Pathan AS, Bilami A, Athmani S (2016) SDAW: secure data aggregation watermarking-based scheme in homogeneous WSNs. Telecommun Syst 62(2):277–288CrossRefGoogle Scholar
  6. Cam H, Ozdemir S, Nair P, Muthuavinashiappan D, Sanli HO (2006) Energy-efficient secure pattern based data aggregation for wireless sensor networks. Comput Commun 29(4):446–455CrossRefGoogle Scholar
  7. Ch SA, Mehmood Z, Rashid Amin D, Alghobiri M, Malik TA (2010) Ensuring reliability and freshness in wireless sensor networks. In: 2010 international conference on intelligent network and computing (ICINC 2010)Google Scholar
  8. Coron JS, Naccache D, Tibouchi M (2012) Public key compression and modulus switching for fully homomorphic encryption over the integers. In: International association for cryptologic research, pp 446–464Google Scholar
  9. Dijk MV, Gentry C, Halevi S, Vaikuntanathan V (2010) Fully homomorphic encryption over the integers. In: Proceedings of the 29th annual international conference on the theory and applications of cryptographic techniques (EUROCRYPT’10), Riviera, France, pp 24–43Google Scholar
  10. Du WL, Deng H, Han YS, Varshney PK (2003) A witness-based approach for data fusion assurance in wireless sensor networks. In: Proceedings of the IEEE global telecommunications conference (GLOBECOM’03), San Francisco, CA, USA, pp 1435–1439Google Scholar
  11. Elhoseny M, Yuan X, El-Minir HK, Riad AM (2016a) An energy efficient encryption method for secure dynamic WSN. Secur Commun Netw 9(13):2024–2031Google Scholar
  12. Elhoseny M, Elminir H, Riad A, Yuan X (2016b) A secure data routing schema for WSN using elliptic curve cryptography and homomorphic encryption. J King Saud Univ 28(3):262–275Google Scholar
  13. Gavinho Filho J, Silva GP, Miceli C et al. (2016) A public key compression method for fully homomorphic encryption using genetic algorithm. In: IEEEGoogle Scholar
  14. Gentry C (2009) Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41th ACM symposium on theory of computing (STOC’09), Bethesda, MD, USA, pp 169–178Google Scholar
  15. Gilad-Bachrach R (2016) Cryptonets: applying neural networks to encrypted data with high throughput and accuracyGoogle Scholar
  16. Harn L, Lin CL (2010) Authenticated group key transfer protocol based on secret sharing. IEEE Trans Comput 59:842–846MathSciNetCrossRefGoogle Scholar
  17. Hevin Rajesh D, Paramasivan B (2015) Data aggregation framework for clustered sensor networks using multi layer perceptron neural network. Int J Adv Res Comput Eng Technol 4:1156–1160Google Scholar
  18. Hu L, Evans D (2003) Secure aggregation for wireless networks. In: Proceedings of workshop on security and assurance in ad hoc networks, Jan 28, Orlando, FLGoogle Scholar
  19. Hu N, Randy RKS, Bradford PG (2004) Security for fixed sensor networks. In: Proceedings of the 42nd annual Southeast regional conference, ACM Press, 2004, NY, USAGoogle Scholar
  20. Karlofc S, Wagner TS (2004) A link layer security architecture for wireless sensor networks. In: International conference on embedded networked sensor systems, Baltimore, MD, USA, Nov 2004, ACM Press, New York, USA, pp 162–175Google Scholar
  21. Katz J, Yung M (2007) Scalable protocols for authenticated group key exchange. J Cryptol 20:85–113MathSciNetCrossRefGoogle Scholar
  22. Khan SA, Aggarwal RK, Kulkarni S (2019) Enhanced homomorphic encryption scheme with PSO for encryption of cloud data. In: IEEEGoogle Scholar
  23. Kumar TS (2019) Efficient resource allocation and QOS enhancements of IoT with FOG network. J ISMAC 1:101–110Google Scholar
  24. Niu SF, Wang CF, Yu ZX, Cao S (2013) Lossy data aggregation integrity scheme in wireless sensor networks. Comput Electr Eng 39:1726–1735CrossRefGoogle Scholar
  25. Ozdemir S (2007) Concealed data aggregation in heterogeneous sensor networks using privacy homomorphism. In: Proceedings of ICPS’07, IEEE international conference on pervasive services, Istanbul, Turkey, pp 165–168Google Scholar
  26. Ozdemir S, Cam H (2010a) Integration of false data detection with data aggregation and confidential transmission in wireless sensor networks. IEEE/ACM Trans Netw 18:736–749CrossRefGoogle Scholar
  27. Ozdemir S, Cam H (2010b) Integration of false data detection with data aggregation and confidential transmission in wireless sensor networks. IEEE/ACM Trans 18:736–749CrossRefGoogle Scholar
  28. Ozdemir S, Yang X (2011) Integrity protecting hierarchical concealed data aggregation for wireless sensor networks. Comput Netw 55:1735–1746CrossRefGoogle Scholar
  29. Sang Y, Shen H (2006) Secure data aggregation in wireless sensor networks. In: IEEE proceedings of the seventh international conference on parallel and distributed computing, applications and technologies (PDCAT’06) 0-7695-2736-1/06Google Scholar
  30. Seetharam D, Rhee S (2004) An efficient pseudo random number generator for low power sensor networks. In: Proceedings of the 29th annual IEEE international conference on local computer networks, Washington, DC, USA, pp 560–562Google Scholar
  31. Shehzad A, Mian O, Iftikhar AK, Tahir AM (2011) Secure data aggregation in wireless sensor networks. In: 3rd international conference on machine learning and computingGoogle Scholar
  32. Smys S, Ranganathan G (2019) Robot assisted sensing, control and manufacture in automobile industry. J ISMAC 1(03):180–187Google Scholar
  33. Suraj M, Raja B, Vengattaraman T (2016) Secure data aggregation in WSN using trust model. Int J Comput Sci Trends Technol 3:130–137Google Scholar
  34. Westhoff D, Girao J, Acharya M (2006a) Concealed data aggregation for reverse multicast traffic in sensor networks: encryption, key distribution and routing adaptation. IEEE Trans Mob Comput 5(10):1417–1431CrossRefGoogle Scholar
  35. Westhoff D, Girao J, Acharya M (2006b) Concealed data aggregation for reverse multicast traffic in sensor networks: encryption, key distribution, and routing adaptation. IEEE Trans Mob Comput 5:1417–1431CrossRefGoogle Scholar
  36. Wu K, Dreef D, Sun B, Xiao Y (2007) Secure data aggregation without persistent cryptographic operations in wireless sensor networks. Ad Hoc Netw 5(1):100–111CrossRefGoogle Scholar
  37. Zhou Q, Yang G, He LW (2014) An efficient secure data aggregation based on homomorphic primitives in wireless sensor networks. Int J Distrib Sens Netw 96:2925Google Scholar
  38. Zhu S, Setia S, Jajodia S, Ning P (2007) Interleaved hop-by-hop authentication against false data injection attacks in sensor networks. ACM Trans Sens Netw. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSNS College of TechnologyCoimbatoreIndia

Personalised recommendations