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<p>This work proposes a novel IoT integrated photonic sensor architecture leveraging swarm optimization to enhance real time cancer cell differentiation via refractive index shifts. A photonic sensor system, which utilizes wavelength shift of precise resonance, is designed to detect cancerous cells (HeLa, Jurkat, MDA MB 231, and MCF7). The sensitivity, specificity and quality factor of the sensor were examined to assess detection accuracy. IoT interconnection allowed monitoring, remote accessibility, and data driven decision process. Alerts were set up to detect errors in the resonant wavelength, and this improved the diagnostic accuracy. In particular, batching and swarm optimization methods were also used to optimize sensor parameters and IoT settings to obtain efficient data transmission and system scalability. Results has shown an excellent level of sensitivity up to 352&#xa0;nm/RIU. This work demonstrates the promise of integrated photonic sensors with Internet of Things (IoT) functionality for high performance, readily available, and certain cancer diagnostics, and paves the way for smart healthcare monitoring systems.</p>

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Design and Analysis of IoT Integrated Photonic Sensors for Cancer Detection

  • 期刊论文

  • 发表日期：

    2025年8月20日

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  • Sheethal Raj T G,
  • Nirmala Hiremani

摘要

This work proposes a novel IoT integrated photonic sensor architecture leveraging swarm optimization to enhance real time cancer cell differentiation via refractive index shifts. A photonic sensor system, which utilizes wavelength shift of precise resonance, is designed to detect cancerous cells (HeLa, Jurkat, MDA MB 231, and MCF7). The sensitivity, specificity and quality factor of the sensor were examined to assess detection accuracy. IoT interconnection allowed monitoring, remote accessibility, and data driven decision process. Alerts were set up to detect errors in the resonant wavelength, and this improved the diagnostic accuracy. In particular, batching and swarm optimization methods were also used to optimize sensor parameters and IoT settings to obtain efficient data transmission and system scalability. Results has shown an excellent level of sensitivity up to 352 nm/RIU. This work demonstrates the promise of integrated photonic sensors with Internet of Things (IoT) functionality for high performance, readily available, and certain cancer diagnostics, and paves the way for smart healthcare monitoring systems.