An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network
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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.
KeywordsSecurity 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.
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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.
All referred studies are highlighted in the literature review.
- 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
- Bagci H, Yazici A (2013) An Energy Aware fuzzy approach to unequal clustering in wireless sensor networks. Elsevier, Amsterdam, pp 1741–1749Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Gilad-Bachrach R (2016) Cryptonets: applying neural networks to encrypted data with high throughput and accuracyGoogle Scholar
- 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
- 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
- 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
- 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
- Khan SA, Aggarwal RK, Kulkarni S (2019) Enhanced homomorphic encryption scheme with PSO for encryption of cloud data. In: IEEEGoogle Scholar
- Kumar TS (2019) Efficient resource allocation and QOS enhancements of IoT with FOG network. J ISMAC 1:101–110Google Scholar
- 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
- 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
- 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
- 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
- Smys S, Ranganathan G (2019) Robot assisted sensing, control and manufacture in automobile industry. J ISMAC 1(03):180–187Google Scholar
- 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
- 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