A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks

Abstract

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

A Survey of Trust Management Schemes for Social Internet of Things

“…He et al [38] developed a trust-up mechanism for underwater acoustic sensor networks (UASNs). An environmental model designed considering the impact of the underwater environment, such as mobility of water flow and the instability nature of acoustic communication, was considered for trust update.…”

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

“…Multi-class supervised learning algorithms such as SVM and random forest [65,56,111] are used to find the optimal decision boundary between trusted and untrusted clusters. Moreover, reinforcement learning [95,52,58,107,168] can be used to find the optimal trust evaluation policy and improve trust evaluation models by continuously interacting with the surrounding environment by trial and error [165].…”

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

“…Similar concept is also observed in fuzzy logic-based secure data transmission, 10 Kalman filter, 11 decision tree 12 and reinforcement learning. 13 It has been noticed that every trust or reputation calculation method needs a threshold value to decide the node's dependency for reliable transmission. This threshold value is human experience and knowledge dependent.…”

“…The advanced reinforcement learning algorithms are proved to be a promising scheme for route selection in the WSN. 7,[13][14][15] Different reward functions have been specified for various action spaces in RL. The advantage of RL schemes is that each node does not need to train the network separately; instead, each node can use the trained RL policy by the main base station or any backbone cloud service to decide the next hope ID.…”