A trust model based on cloud theory in underwater acoustic sensor networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Jinfang Jiang
  • Guangjie Han
  • Lei Shu
  • Sammy Chan
  • Kun Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number7360179
Pages (from-to)342-350
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume13
Issue number1
Online published17 Dec 2015
Publication statusPublished - Feb 2017

Abstract

Underwater acoustic sensor networks (UASNs) are susceptible to a large number of security threats, e.g., jamming attacks at physical layer, collision attacks at data link layer and DoS attacks at network layer. Because of the communication, computation and storage constraints of underwater sensor nodes, traditional security mechanisms, e.g., encryption algorithms, are not suitable for UASNs. A trust model has been recently suggested as an effective security mechanism for open environments such as terrestrial wireless sensor networks (TWSNs), and considerable research has been done on modeling and managing trust relationship among sensor nodes. However, the trust models proposed for TWSNs cannot be directly used in a UASN due to its unique characterizes such as unreliable acoustic channel, dynamic network structure, and weak link connectivity. In this paper, we propose a novel Trust Model based on Cloud theory (TMC) for UASNs. The objective of TMC is to solve uncertainty and fuzziness of trust based on cloud theory, which ultimately improves trust evaluation accuracy. Also, simulation results demonstrate that our algorithm outperforms other related works in terms of detection ratio of malicious nodes, successful packet delivery ratio and network lifetime.

Research Area(s)

  • Cloud theory, Trust model, Underwater acoustic sensor networks

Citation Format(s)

A trust model based on cloud theory in underwater acoustic sensor networks. / Jiang, Jinfang; Han, Guangjie; Shu, Lei et al.
In: IEEE Transactions on Industrial Informatics, Vol. 13, No. 1, 7360179, 02.2017, p. 342-350.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review