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<p>Wireless Sensor Networks play a critical role in modern Internet of Things applications, supporting real-time monitoring in domains such as healthcare, environmental sensing, agriculture, and smart cities. However, Wireless Sensor Networks are inherently constrained by limited energy, computational power, and memory, making them highly susceptible to issues like inefficient data transmission, premature node failure, and data security breaches. Existing solutions often fail to balance optimization and security, especially in large-scale Internet of Things environments. To address these challenges, a novel framework integrating a Modified Paillier Additive Homomorphic Encryption scheme with a Levy Gaussian Coati Optimization algorithm is proposed. The Levy Gaussian Coati Optimization algorithm enhances cluster head selection through a hybrid metaheuristic combining the Coati Optimization Algorithm for efficient Cluster Head selection, the Gaussian Barebone mechanism for refined local search precision, and the Levy Flight strategy for improved global search capability. Simultaneously, the Modified Paillier Additive Homomorphic Encryption mechanism enables secure data aggregation and computation on encrypted data without decryption, reducing the risk of data leakage. Additionally, a lightweight data compression technique is introduced to minimize communication overhead and prolong network lifetime in Wireless Sensor Networks. Experimental results demonstrate that the proposed model achieves a 48% packet delivery ratio, reduces energy consumption to 5.4&#xa0;J, security rate of 89%, and significantly outperforms existing state-of-the-art methods across multiple performance metrics. These results validate the framework as a secure, scalable, and energy-efficient solution for next-generation Wireless Sensor Network-based Internet of Things environments.</p>

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A novel homomorphic encryption-based optimization framework for wireless sensor networks

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • S. K. Susee,
  • M. Senthil Kumar,
  • B. Chidambararajan

摘要

Wireless Sensor Networks play a critical role in modern Internet of Things applications, supporting real-time monitoring in domains such as healthcare, environmental sensing, agriculture, and smart cities. However, Wireless Sensor Networks are inherently constrained by limited energy, computational power, and memory, making them highly susceptible to issues like inefficient data transmission, premature node failure, and data security breaches. Existing solutions often fail to balance optimization and security, especially in large-scale Internet of Things environments. To address these challenges, a novel framework integrating a Modified Paillier Additive Homomorphic Encryption scheme with a Levy Gaussian Coati Optimization algorithm is proposed. The Levy Gaussian Coati Optimization algorithm enhances cluster head selection through a hybrid metaheuristic combining the Coati Optimization Algorithm for efficient Cluster Head selection, the Gaussian Barebone mechanism for refined local search precision, and the Levy Flight strategy for improved global search capability. Simultaneously, the Modified Paillier Additive Homomorphic Encryption mechanism enables secure data aggregation and computation on encrypted data without decryption, reducing the risk of data leakage. Additionally, a lightweight data compression technique is introduced to minimize communication overhead and prolong network lifetime in Wireless Sensor Networks. Experimental results demonstrate that the proposed model achieves a 48% packet delivery ratio, reduces energy consumption to 5.4 J, security rate of 89%, and significantly outperforms existing state-of-the-art methods across multiple performance metrics. These results validate the framework as a secure, scalable, and energy-efficient solution for next-generation Wireless Sensor Network-based Internet of Things environments.