A smart and fast fuzzy based-trust management scheme in underwater acoustic sensor networks (UASNs)

초록

Underwater acoustic sensor networks (UASNs) are increasingly employed in both military and civilian applications. Owing to the challenging nature of underwater communication-characterized by unstable channels and dynamic environmental factors- UASNs are vulnerable to various security threats. Therefore, designing robust and adaptive trust management frameworks is vital to ensure their secure and stable operation. This paper introduces a novel, smart, and fast, fuzzy based-trust management (SFFTM) scheme in UASNs. In SFFTM, trust values are derived from three types of trust evidence: communication-based, energy-based and, data-based. To refine communication-based trust evidence, SFFTM distinguishes between communication failures caused by malicious nodes and those resulting from channel instability. To achieve this, a new control factor- combining link delay and link usage- is introduced. When failures are caused by malicious nodes, this factor exponentially reduces the trust evidence. Conversely, if the failures result from instability, the control factor enhances the trust evidence. Furthermore, the energy-based trust evidence is derived by analyzing the residual energy of each nod and evaluating the correlation between its actual and expected energy consumption rates using the Pearson correlation coefficient (PCC). SFFTM includes four trust elements: direct trust, recommendation trust, integrated trust, and indirect trust. Direct trust is obtained from a fuzzy inference system that processes the three trust evidences. Recommendation trust is determined by evaluating the honesty degree of recommenders and assigning higher weights to those with higher honesty level. Indirect trust is inferred through a multi-hop trust chain that links the evaluator to the target node, where intermediate recommenders are chosen according to both their spatial proximity and their assessed trustworthiness. The performance of SFFTM is evaluated through extensive simulations and compared with three existing approaches- CFFTM, LTrust, and TMC- under three attack scenarios: bad/good-mouthing, collision, and hybrid attacks. Simulation findings show that SFFTM outperforms the existing techniques by enhancing detection accuracy and significantly reducing the false alarm rate. Specifically, in the first scenario, SFFTM improves the indirect trust of legitimate nodes by 2.58%, increases detection accuracy by 2.08%, and reduces the false detection rate by 21.13%. In the second scenario, it enhances the indirect trust of the hostile nodes by 5.45%, increases detection accuracy by 1.91%, and reduces false alarms by 15.29%. In the third scenario, detection accuracy is evaluated by 2.52%, while the false detection rate is reduced by 41.46%.

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