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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.


First example of node calculation.
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sensors-15-16763-f002: First example of node calculation.

Mentions: After calculation, the just-completed node is updated as the parent node, and the root node continues to regard the node in the sub-tree cluster that is nearest to the parent node as the child node. The root node then begins the next round of calculation. If Dis_horizontal(parent, son) > Rb or Depth(son) is larger than the depth of the monitored area, then the parent node is set as the root node and the dive depth of the child node is recalculated. If the leaf node is the next root node, then its parent node is also set as the root node; meanwhile, the maximum depth is limited to Rs. As shown in Figure 2 and Figure 3, the circle with the red side is the root node, the circle with the yellow side is the next root node, the pure blue circle represents the parent node, the pure green circle is the child node, the pure grey circle denotes that node depth has been calculated, and the black arrows indicate the calculation order in the sub-tree. As shown in Figure 2, following the normal sequence, when the depth of node D is calculated, the parent node should be node C (blue and white circles in Figure 2), but the result obtained is Depth(D) > Depth(water). Thus, the parent node of node D is updated as the root node and the depth of node D is recalculated, as shown in Figure 2. Next, the root node calculates the depth of node E, and node D becomes the parent node, as indicated in the normal order (blue and white circles in Figure 3). However, Dis_horizontal (D, E) > Rb; thus, the parent node is updated as the root node, as shown in Figure 3. When the depth of node F is calculated, its parent node is also updated as the root node.


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

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

First example of node calculation.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16763-f002: First example of node calculation.
Mentions: After calculation, the just-completed node is updated as the parent node, and the root node continues to regard the node in the sub-tree cluster that is nearest to the parent node as the child node. The root node then begins the next round of calculation. If Dis_horizontal(parent, son) > Rb or Depth(son) is larger than the depth of the monitored area, then the parent node is set as the root node and the dive depth of the child node is recalculated. If the leaf node is the next root node, then its parent node is also set as the root node; meanwhile, the maximum depth is limited to Rs. As shown in Figure 2 and Figure 3, the circle with the red side is the root node, the circle with the yellow side is the next root node, the pure blue circle represents the parent node, the pure green circle is the child node, the pure grey circle denotes that node depth has been calculated, and the black arrows indicate the calculation order in the sub-tree. As shown in Figure 2, following the normal sequence, when the depth of node D is calculated, the parent node should be node C (blue and white circles in Figure 2), but the result obtained is Depth(D) > Depth(water). Thus, the parent node of node D is updated as the root node and the depth of node D is recalculated, as shown in Figure 2. Next, the root node calculates the depth of node E, and node D becomes the parent node, as indicated in the normal order (blue and white circles in Figure 3). However, Dis_horizontal (D, E) > Rb; thus, the parent node is updated as the root node, as shown in Figure 3. When the depth of node F is calculated, its parent node is also updated as the root node.

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.