An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks
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ID: 110222
2020
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Abstract
Underwater sensor networks (UWSNs) have recently attracted much attention due to their ability to discover and monitor the aquatic environment. However, acoustic communication has posed some significant challenges, such as high propagation delay, low available bandwidth, and high bit error rate. Therefore, proposing a cross-layer protocol is of high importance to the field to integrate different communication functionalities (i.e, an interaction between data link layer and network layer) to interact in a more reliable and flexible manner to overcome the consequences of applying acoustic signals. In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance by providing the interaction between the MAC and routing layers. In CLMD, an Autonomous Underwater Vehicle (AUV) is used to periodically visit a group of clusters which are responsible for data collection from members. The communications are managed by using a distributed cross-layer solution to enhance network performance in terms of packet delivery and energy saving. The cluster heads are replaced with other candidate members at the end of each operational phase to prolong the network lifetime. The effectiveness of CLMD is verified through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio with varying number of nodes. The effects of MAC protocols are also studied by studying the network performance under various MAC protocols in terms of packet delivery ratio, goodput, and energy consumption with varying density of nodes.
| Reference Key |
alfouzan2020sensorsan
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| Authors | Faisal Abdulaziz Alfouzan;Seyed Mohammad Ghoreyshi;Alireza Shahrabi;Mahsa Sadeghi Ghahroudi;Alfouzan, Faisal Abdulaziz;Ghoreyshi, Seyed Mohammad;Shahrabi, Alireza;Ghahroudi, Mahsa Sadeghi; |
| Journal | sensors |
| Year | 2020 |
| DOI |
10.3390/s20174813
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| URL | |
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