Scholarlink

<p>In Internet of Things (IoT)-based healthcare systems, protecting patient privacy is of paramount importance. Prior studies have addressed this concern extensively through end-to-end privacy-preserving frameworks that secure both patient data and location information, often under the assumption that system entities are either not fully developed or cannot be completely trusted. To advance this line of work, the present research introduces a Blockchain-Integrated Lightweight Phase Convolutional Spiking Neural Network with the Pine Cone Optimization Algorithm (LPC-SNN-PCOA) to enable secure data transmission and enhance Source Location Privacy (SLP) within wireless sensor networks. The system utilizes the Proof-of-Work Consensus Blockchain (PoWCB) for immutable, decentralized data storage. At the same time, Prism Refraction Search (PRS) optimization dynamically chooses phantom nodes based on network conditions, distance, trust, and energy. Multi-path routing is more reliable, and the Lightweight Phase Convolutional Spiking Neural Network (LPC-SNN) enables optimal route selection based on temporal–spatial information. The Pine Cone Optimization Algorithm is a treatment for effective and safe data transfer. The proposed system achieved a network lifetime of 101.820&#xa0;s, a safety period of 664,979.4&#xa0;m, 0.005&#xa0;J of energy consumption, and a 99.8% packet delivery ratio, surpassing the benchmark models in energy efficiency, privacy, and precise routing. The architecture is tested through simulation of 100 nodes in a 100 × 100&#xa0;m area, demonstrating its usefulness in ensuring the secure, real-time delivery of healthcare information.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Blockchain-enabled spiking neural network for optimal routing and location privacy protection in IoT healthcare networks

  • 期刊论文

  • 发表日期：

    2026年1月19日

查看全文
  • Arockiasamy Punitha,
  • Madhu Kumar Vanteru,
  • Kaliyaperumal Manojkumar,
  • Tappeta Vinay Simha Reddy

摘要

In Internet of Things (IoT)-based healthcare systems, protecting patient privacy is of paramount importance. Prior studies have addressed this concern extensively through end-to-end privacy-preserving frameworks that secure both patient data and location information, often under the assumption that system entities are either not fully developed or cannot be completely trusted. To advance this line of work, the present research introduces a Blockchain-Integrated Lightweight Phase Convolutional Spiking Neural Network with the Pine Cone Optimization Algorithm (LPC-SNN-PCOA) to enable secure data transmission and enhance Source Location Privacy (SLP) within wireless sensor networks. The system utilizes the Proof-of-Work Consensus Blockchain (PoWCB) for immutable, decentralized data storage. At the same time, Prism Refraction Search (PRS) optimization dynamically chooses phantom nodes based on network conditions, distance, trust, and energy. Multi-path routing is more reliable, and the Lightweight Phase Convolutional Spiking Neural Network (LPC-SNN) enables optimal route selection based on temporal–spatial information. The Pine Cone Optimization Algorithm is a treatment for effective and safe data transfer. The proposed system achieved a network lifetime of 101.820 s, a safety period of 664,979.4 m, 0.005 J of energy consumption, and a 99.8% packet delivery ratio, surpassing the benchmark models in energy efficiency, privacy, and precise routing. The architecture is tested through simulation of 100 nodes in a 100 × 100 m area, demonstrating its usefulness in ensuring the secure, real-time delivery of healthcare information.