As a candidate trust management scheme, trust models based on the cloud theory are always taken into account when detecting malicious attacks in Underwater Acoustic Sensor Networks (UASNs). To evaluate the trust values of nodes accurately, the evidence of trust ought to be collected frequently. As a result, continual trust update results in excessive energy consumption or premature death of some sensor nodes that are close to the trust cloud node. To address the above issues, in this paper, we propose an Energy-balanced Trust Cloud Migration scheme (ETCM) for UASNs, which consists of Destination Node Determination (DND), trust cloud migration and trust cloud update. Particularly, DND is performed hierarchically to obtain the destination node for trust cloud migration by selecting the candidate destination clusters, determining the destination cluster and the destination node, respectively. First, the candidate destination clusters are selected based on the distribution for the overall residual energy in UASNs using the simulated annealing algorithm. Then, an indicator of Cluster Ability (CA) is proposed to seek the destination cluster. Particularly, to calculate CA, the improved standardized Euclidean distance formula is employed to evaluate the connectivity between clusters and their neighboring clusters. Finally, on the basis of the defined node density reachability and the residual energy, the Node Ability (NA) is presented to indicate the capacity of nodes for trust cloud storage, calculation and update. The destination node in the destination cluster can be determined as the new trust cloud node using the NA. Simulation results demonstrate that the proposed ETCM scheme can balance energy consumption, increase node survival ratio and prolong lifetime effectively.

中文翻译：

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水声传感器网络能量平衡信任云迁移方案

作为候选的信任管理方案，基于云理论的信任模型在检测水声传感器网络（UASNs）中的恶意攻击时总是被考虑在内。为了准确评估节点的信任值，需要经常收集信任证据。因此，持续的信任更新会导致一些靠近信任云节点的传感器节点过度消耗能量或过早死亡。针对上述问题，本文提出了一种用于UASNs的能量平衡信任云迁移方案（ETCM），该方案由目的地节点确定（DND）、信任云迁移和信任云更新组成。具体来说，通过选择候选目标集群，分层进行DND以获得信任云迁移的目标节点，分别确定目标集群和目标节点。首先，使用模拟退火算法根据 UASN 中整体剩余能量的分布选择候选目标簇。然后，提出了集群能力（CA）指标来寻找目标集群。特别地，为了计算CA，采用改进的标准化欧几里德距离公式来评估簇与其相邻簇之间的连通性。最后，在定义的节点密度可达性和剩余能量的基础上，提出节点能力（NA）来表示节点对信任云存储、计算和更新的能力。目的集群中的目的节点可以通过NA确定为新的信任云节点。