Adaptive Node Clustering for Underwater Sensor Networks

Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-06, Vol.21 (13), p.4514
Main Authors: Khan, Muhammad Fahad, Bibi, Muqaddas, Aadil, Farhan, Lee, Jong-Weon
Format: Article
Language:English
Subjects:
adaptive node clustering technique
dragonfly optimization
nodes clustering
optimized routing
transmission range
underwater sensor networks
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Summary:Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21134514