Dolphin swarm optimization algorithm for software‐defined antenna selection algorithm in underwater acoustic sensor network
Summary Underwater acoustic sensor network (UASN) employs to monitor the aquatic environment. The underwater nodes deploy for continuous and real‐time monitoring. The underwater nodes deploy at different depths to facilitate data forwarding from ocean floor sensor to buoys node. However, the underwa...
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Published in: | International journal of communication systems Vol. 34; no. 12 |
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Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Chichester
Wiley Subscription Services, Inc
01-08-2021
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Subjects: | |
Online Access: | Get full text |
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Summary: | Summary
Underwater acoustic sensor network (UASN) employs to monitor the aquatic environment. The underwater nodes deploy for continuous and real‐time monitoring. The underwater nodes deploy at different depths to facilitate data forwarding from ocean floor sensor to buoys node. However, the underwater network has limited network resources such as the battery, bandwidth, and susceptibility to mobility, thermographic conditions, and propagation delay. The propagation delay and thermographic condition prolong node packet processing and delay network operation. The delayed network operation increases node battery consumption and overhead. Hence, a bio‐inspired dolphin swarm optimization algorithm (DSA) implements to select the optimal antenna for data transmission. The DSA selects the optimal antenna for data communication by evaluating the acknowledgment packet obtained for data transmitted through multiband antennas. The DSA was simulated in an NS2‐Aquasim environment and testbed to evaluate its performance. The DSA improved data reliability, packet delivery ratio by 97%, and reduced bit error rate (BER), end‐to‐end delay compared to traditional algorithms.
A bio‐inspired DSA was implemented to select an optimal antenna for data transmission to improve data reliability. The DSA performed better in terms of PDR, throughput and energy consumption compared to traditional algorithms. The BER was in the range of 5.3 to 2, which is lower compared to traditional algorithms. |
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ISSN: | 1074-5351 1099-1131 |
DOI: | 10.1002/dac.4903 |