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    An energy-balanced trust cloud migration scheme for underwater acoustic sensor networks

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    Date
    2020-03-01
    Author
    Han, Guangjie
    Du, Jiaxin
    Lin, Chuan
    Wu, Hongyi
    Guizani, Mohsen
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    Abstract
    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.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081724066&origin=inward
    DOI/handle
    http://dx.doi.org/10.1109/TWC.2019.2955652
    http://hdl.handle.net/10576/37262
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    • Computer Science & Engineering [‎2428‎ items ]

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