Jamming Attacks Detection and Ejection in Over the Air Computation Concentrated Solar Power Systems

The integration of the Over-the-Air Computation (AirComp) in Concentrated Solar Power (CSP) systems offers a transformative potential for real-time data aggregation in 6G-IoT networks. However, such an innovative deployment introduces critical vulnerabilities to jamming attacks that distort the aggregated signals without detection. This paper introduces a robust security framework that leverages an artificial intelligence (AI) statistical signal analysis, adaptive detection thresholds, and spatially-aware mitigation to detect and eject both simple and coordinated jamming attacks in real-world AirComp CSP systems. Detailed experimental validation at the National Solar Thermal Test Facility demonstrates significant improvements in the data aggregation and attack detection rates. It also ensures trustworthy data aggregation from commercial CSP fields, while maintaining their operational resilience.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-09-04
DOI
https://doi.org/10.1109/jiot.2025.3606344
Citations
2
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
4.54
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article

Jamming Attacks Detection and Ejection in Over the Air Computation Concentrated Solar Power Systems

Eirini Eleni Tsiropoulou, Jim Plusquellic, Dipanjan Adhikary
2 citations
IEEE Internet of Things Journal
Physical Unclonable Functions (PUFs) and Hardware Security
4.54
article

Jamming Attacks Detection and Ejection in Over the Air Computation Concentrated Solar Power Systems

Eirini Eleni Tsiropoulou, Jim Plusquellic, Dipanjan Adhikary
article en
2 citations

Abstract

The integration of the Over-the-Air Computation (AirComp) in Concentrated Solar Power (CSP) systems offers a transformative potential for real-time data aggregation in 6G-IoT networks. However, such an innovative deployment introduces critical vulnerabilities to jamming attacks that distort the aggregated signals without detection. This paper introduces a robust security framework that leverages an artificial intelligence (AI) statistical signal analysis, adaptive detection thresholds, and spatially-aware mitigation to detect and eject both simple and coordinated jamming attacks in real-world AirComp CSP systems. Detailed experimental validation at the National Solar Thermal Test Facility demonstrates significant improvements in the data aggregation and attack detection rates. It also ensures trustworthy data aggregation from commercial CSP fields, while maintaining their operational resilience.

IEEE Internet of Things JournalVol. 13(5)
University of New Mexico (US), Arizona State University (US)
Openalex Percentile: Top 5%
Physical Unclonable Functions (PUFs) and Hardware Security
4.54
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