SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication
Machine-to-machine (M2M) communication within the Internet of Things (IoT) faces increasing security and efficiency challenges as networks proliferate. Existing approaches often struggle with balancing robust security measures and energy efficiency, leading to vulnerabilities and reduced performance...
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| Format: | Article |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2551.pdf |
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| author | Yusuf Kursat Tuncel Kasım Öztoprak |
| author_facet | Yusuf Kursat Tuncel Kasım Öztoprak |
| author_sort | Yusuf Kursat Tuncel |
| collection | DOAJ |
| description | Machine-to-machine (M2M) communication within the Internet of Things (IoT) faces increasing security and efficiency challenges as networks proliferate. Existing approaches often struggle with balancing robust security measures and energy efficiency, leading to vulnerabilities and reduced performance in resource-constrained environments. To address these limitations, we propose SAFE-CAST, a novel secure AI-federated enumeration for clustering-based automated surveillance and trust framework. This study addresses critical security and efficiency challenges in M2M communication within the context of IoT. SAFE-CAST integrates several innovative components: (1) a federated learning approach using Lloyd’s K-means algorithm for secure clustering, (2) a quality diversity optimization algorithm (QDOA) for secure channel selection, (3) a dynamic trust management system utilizing blockchain technology, and (4) an adaptive multi-agent reinforcement learning for context-aware transmission scheme (AMARLCAT) to minimize latency and improve scalability. Theoretical analysis and extensive simulations using network simulator (NS)-3.26 demonstrate the superiority of SAFE-CAST over existing methods. The results show significant improvements in energy efficiency (21.6% reduction), throughput (14.5% increase), security strength (15.3% enhancement), latency (33.9% decrease), and packet loss rate (12.9% reduction) compared to state-of-the-art approaches. This comprehensive solution addresses the pressing need for robust, efficient, and secure M2M communication in the evolving landscape of IoT and edge computing. |
| format | Article |
| id | doaj-art-0cb6b974f8f748f39dfe6b2cc4a48bbc |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-0cb6b974f8f748f39dfe6b2cc4a48bbc2025-01-04T15:05:21ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e255110.7717/peerj-cs.2551SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communicationYusuf Kursat Tuncel0Kasım Öztoprak1Department of Computer Engineering, Konya Food And Agriculture University, Konya, TurkeyDepartment of Computer Engineering, Konya Food And Agriculture University, Konya, TurkeyMachine-to-machine (M2M) communication within the Internet of Things (IoT) faces increasing security and efficiency challenges as networks proliferate. Existing approaches often struggle with balancing robust security measures and energy efficiency, leading to vulnerabilities and reduced performance in resource-constrained environments. To address these limitations, we propose SAFE-CAST, a novel secure AI-federated enumeration for clustering-based automated surveillance and trust framework. This study addresses critical security and efficiency challenges in M2M communication within the context of IoT. SAFE-CAST integrates several innovative components: (1) a federated learning approach using Lloyd’s K-means algorithm for secure clustering, (2) a quality diversity optimization algorithm (QDOA) for secure channel selection, (3) a dynamic trust management system utilizing blockchain technology, and (4) an adaptive multi-agent reinforcement learning for context-aware transmission scheme (AMARLCAT) to minimize latency and improve scalability. Theoretical analysis and extensive simulations using network simulator (NS)-3.26 demonstrate the superiority of SAFE-CAST over existing methods. The results show significant improvements in energy efficiency (21.6% reduction), throughput (14.5% increase), security strength (15.3% enhancement), latency (33.9% decrease), and packet loss rate (12.9% reduction) compared to state-of-the-art approaches. This comprehensive solution addresses the pressing need for robust, efficient, and secure M2M communication in the evolving landscape of IoT and edge computing.https://peerj.com/articles/cs-2551.pdfMachine-to-machine communicationInternet of things securityFederated learningBlockchain trust managementQuantum-derived optimizationSecure clustering |
| spellingShingle | Yusuf Kursat Tuncel Kasım Öztoprak SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication PeerJ Computer Science Machine-to-machine communication Internet of things security Federated learning Blockchain trust management Quantum-derived optimization Secure clustering |
| title | SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication |
| title_full | SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication |
| title_fullStr | SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication |
| title_full_unstemmed | SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication |
| title_short | SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication |
| title_sort | safe cast secure ai federated enumeration for clustering based automated surveillance and trust in machine to machine communication |
| topic | Machine-to-machine communication Internet of things security Federated learning Blockchain trust management Quantum-derived optimization Secure clustering |
| url | https://peerj.com/articles/cs-2551.pdf |
| work_keys_str_mv | AT yusufkursattuncel safecastsecureaifederatedenumerationforclusteringbasedautomatedsurveillanceandtrustinmachinetomachinecommunication AT kasımoztoprak safecastsecureaifederatedenumerationforclusteringbasedautomatedsurveillanceandtrustinmachinetomachinecommunication |