SecLMMS: Secure AGI-enabled low-altitude maritime monitoring system
Publication Type
Journal Article
Publication Date
1-2026
Abstract
Artificial General Intelligence (AGI) offers transformative potential for low-altitude maritime monitoring systems (LMMS), enabling enhanced situational awareness, intelligent UAV coordination, and adaptive maritime operations. However, leveraging AGI in LMMS introduces critical challenges, particularly in ensuring secure, efficient, and privacy-preserving signature aggregation in bandwidth-constrained environments. In this paper, we present SecLMMS, a secure and AGI-enabled LMMS architecture tailored for the low-altitude economy. SecLMMS fuses real-time and offline AGI-driven decision-making to achieve both responsive control and strategic maritime insight. To minimize the computational and communication burden in AGI-enhanced UAV networks, we design a novel compound aggregate signature scheme with dual-layer aggregation, offering strong data authenticity guarantees while reducing public key overhead. SecLMMS further supports multimodal data collection and redaction, safeguarding against replay attacks, Sybil attacks, and privacy leaks. Formal analysis confirms the system’s security and privacy resilience, while extensive experiments demonstrate substantial reductions in system overhead. SecLMMS represents a promising step toward scalable, secure, and intelligent AGI-driven maritime monitoring.
Keywords
aggregate signature, Artificial general intelligence, maritime monitoring, UAV network
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Transactions on Cognitive Communications and Networking
Volume
12
First Page
3335
Last Page
3347
Identifier
10.1109/TCCN.2025.3620350
Publisher
Institute of Electrical and Electronics Engineers
Citation
XUE, Wenyi; YANG, Yang; HUANG, Hongwu; GAO, Xiujing; DONG, Chen; PANG, Hwee Hwa; and DENG, Robert H..
SecLMMS: Secure AGI-enabled low-altitude maritime monitoring system. (2026). IEEE Transactions on Cognitive Communications and Networking. 12, 3335-3347.
Available at: https://ink.library.smu.edu.sg/sis_research/11067
Additional URL
https://doi.org/10.1109/TCCN.2025.3620350