A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment
Journal of Marine Science and Engineering, ISSN: 2077-1312, Vol: 11, Issue: 7
2023
- 2Citations
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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JMSE, Vol. 11, Pages 1354: A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment
JMSE, Vol. 11, Pages 1354: A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment Journal of Marine Science and Engineering doi: 10.3390/jmse11071354
Article Description
The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.
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