Agents for Agents: An Interrogator-Based Secure Framework for Autonomous Internet of Underwater Things

2026-04-05

AI Summary

This study addresses the security limitations of existing underwater Internet of Things (UW-IoT) systems, which rely on static trust mechanisms and struggle to respond to compromised or anomalous nodes. To overcome this, the authors propose an interrogator-based dynamic trust monitoring framework that uniquely integrates lightweight Transformer-driven behavioral trust evaluation with consortium blockchain-enabled identity management. The framework enables real-time computation of dynamic trust scores through passive analysis of communication metadata, without impeding node autonomy, thereby facilitating rapid identification and access restriction of malicious or aberrant agents. Simulation results demonstrate that, compared to static baseline approaches, the proposed method improves detection accuracy by 21.7% while maintaining manageable energy overhead, significantly enhancing the system’s security, robustness, and scalability.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Autonomous underwater vehicles (AUVs) and sensor nodes increasingly support decentralized sensing and coordination in the Internet of Underwater Things (IoUT), yet most deployments rely on static trust once authentication is established, leaving long-duration missions vulnerable to compromised or behaviorally deviating agents. In this paper, an interrogator based structure is presented that incorporates the idea of behavioral trust monitoring into underwater multi-agent operation without interfering with autonomy. Privileged interrogator module is a passive communication metadata analyzer that uses a lightweight transformer model to calculate dynamic trust scores, which are used to authorize the forwarding of mission critical data. Suspicious agents cause proportional monitoring and conditional restrictions, which allow fast containment and maintain network continuity. The evidence of trust is stored in a permissioned blockchain consortium which offers identity management which is not tampered and is decentralized without causing the overhead of public consensus mechanisms. Simulation based analysis shows that the evaluation of the result compares to a relative improvement of 21.7% in the detection accuracy compared to the static trust baselines with limited energy overhead. These findings suggest that behavior driven validation has the capability of reinforcing underwater coordination without compromising scalability and deployment.

Problem

• Internet of Underwater Things
• behavioral trust
• autonomous agents
• security
• trust monitoring

Innovation

• behavioral trust monitoring
• lightweight transformer
• interrogator-based framework
• permissioned blockchain
• autonomous IoUT