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Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks.

Jiang P, Wang X, Jiang L - Sensors (Basel) (2015)

Bottom Line: The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement.Furthermore, the silent mode is adopted to reduce communication cost.Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes.

View Article: PubMed Central - PubMed

Affiliation: Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China. pjiang@hdu.edu.cn.

ABSTRACT
Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments.

No MeSH data available.


Connectivity with varying communication radii.
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sensors-15-16763-f006: Connectivity with varying communication radii.

Mentions: Figure 6 shows the comparison of the connectivity that varies with the Rc of the four algorithms when the number of nodes is 43 and Rs is 10 m. As shown in Figure 6, when Rc is less than 20 m under the same value, SDDA performance is slightly better than that of RAND. CTDA achieves a better result than SDDA and RAND. CDA performs closely with CTDA. This result can be attributed to the fact that SDDA mainly focuses on maximizing network coverage, and that some nodes that are originally connected lose contact with the sink node after depth is adjusted to improve network connectivity. The node does not only continuously adjust depth but also increase energy consumption and possibly decrease coverage. In CTDA, building a connected tree ensures that the node that is initially connected still maintains connectivity after adjusting depth as much as possible. In CDA, the network initially builds a connectivity link, and then, as the leader, the node on the link calculates the depth of the surrounding nodes. When Rc is greater than 20 m, the three algorithms can all achieve full connectivity [32].


Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks.

Jiang P, Wang X, Jiang L - Sensors (Basel) (2015)

Connectivity with varying communication radii.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541905&req=5

sensors-15-16763-f006: Connectivity with varying communication radii.
Mentions: Figure 6 shows the comparison of the connectivity that varies with the Rc of the four algorithms when the number of nodes is 43 and Rs is 10 m. As shown in Figure 6, when Rc is less than 20 m under the same value, SDDA performance is slightly better than that of RAND. CTDA achieves a better result than SDDA and RAND. CDA performs closely with CTDA. This result can be attributed to the fact that SDDA mainly focuses on maximizing network coverage, and that some nodes that are originally connected lose contact with the sink node after depth is adjusted to improve network connectivity. The node does not only continuously adjust depth but also increase energy consumption and possibly decrease coverage. In CTDA, building a connected tree ensures that the node that is initially connected still maintains connectivity after adjusting depth as much as possible. In CDA, the network initially builds a connectivity link, and then, as the leader, the node on the link calculates the depth of the surrounding nodes. When Rc is greater than 20 m, the three algorithms can all achieve full connectivity [32].

Bottom Line: The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement.Furthermore, the silent mode is adopted to reduce communication cost.Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes.

View Article: PubMed Central - PubMed

Affiliation: Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China. pjiang@hdu.edu.cn.

ABSTRACT
Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments.

No MeSH data available.