An Enhanced Encryption Scheme for <scp>IoT</scp>‐Based Wireless Sensor Network Using <scp>DNA</scp> Enclosed Fully Homomorphic Approach

ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has revolutionized wireless sensor networks (WSNs), enabling real‐time monitoring across various applications. However, this growth introduces critical security challenges, including data breaches, time consumption, and memory overhead, which limit the efficiency and scalability of the existing encryption models. To address these issues, this paper proposes a novel DNA‐enclosed Fully Homomorphic Encryption (HD‐FHE) scheme integrated with improved elliptic curve cryptography (IECC). The proposed approach leverages dual‐layer encryption by combining the strengths of deoxyribonucleic acid (DNA) computing and homomorphic encryption to secure data processing without decryption. The IECC further enhances key generation efficiency and reduces resource consumption. The experimental results demonstrate significant improvements in encryption (1.675 ms for 3 KB) and decryption (1.582 ms for 3 KB) times, alongside high throughput (2.275 ms for 7 KB), outperforming the existing models. These results highlight the robustness of the proposed method in minimizing vulnerabilities to Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) while ensuring scalability in dynamic IoT environments. This work provides a significant contribution to IoT‐based WSN security by achieving a balance between performance and protection, paving the way for secure and efficient data transmission in next‐generation networks.

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Publication Details

Journal
Transactions on Emerging Telecommunications Technologies
Published
2025-03-01
DOI
https://doi.org/10.1002/ett.70075
Citations
5
Primary Topic
DNA and Biological Computing
Type
article
Field-Weighted Citation Impact
4.03
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article

An Enhanced Encryption Scheme for IoT‐Based Wireless Sensor Network Using DNA Enclosed Fully Homomorphic Approach

Alka P. Sawlikar, Devashri Raich, B. Ganguly, Lowlesh Nandkishor Yadav
5 citations
Transactions on Emerging Telecommunications Technologies
DNA and Biological Computing
4.03
article

An Enhanced Encryption Scheme for IoT‐Based Wireless Sensor Network Using DNA Enclosed Fully Homomorphic Approach

Alka P. Sawlikar, Devashri Raich, B. Ganguly, Lowlesh Nandkishor Yadav
article en
5 citations

Abstract

ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has revolutionized wireless sensor networks (WSNs), enabling real‐time monitoring across various applications. However, this growth introduces critical security challenges, including data breaches, time consumption, and memory overhead, which limit the efficiency and scalability of the existing encryption models. To address these issues, this paper proposes a novel DNA‐enclosed Fully Homomorphic Encryption (HD‐FHE) scheme integrated with improved elliptic curve cryptography (IECC). The proposed approach leverages dual‐layer encryption by combining the strengths of deoxyribonucleic acid (DNA) computing and homomorphic encryption to secure data processing without decryption. The IECC further enhances key generation efficiency and reduces resource consumption. The experimental results demonstrate significant improvements in encryption (1.675 ms for 3 KB) and decryption (1.582 ms for 3 KB) times, alongside high throughput (2.275 ms for 7 KB), outperforming the existing models. These results highlight the robustness of the proposed method in minimizing vulnerabilities to Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) while ensuring scalability in dynamic IoT environments. This work provides a significant contribution to IoT‐based WSN security by achieving a balance between performance and protection, paving the way for secure and efficient data transmission in next‐generation networks.

Transactions on Emerging Telecommunications TechnologiesVol. 36(3)
Chandra Shekhar Azad University of Agriculture and Technology (IN), Rajiv Gandhi Institute of Petroleum Technology (IN)
Openalex Percentile: Top 7%
DNA and Biological Computing
4.03
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