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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.


Comparison of total moved distance at varying numbers of nodes.
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sensors-15-16763-f008: Comparison of total moved distance at varying numbers of nodes.

Mentions: A 3D coverage is obtained by adjusting the depth of nodes. Obviously, the longer the distance moved by nodes, the greater the amount of energy consumed. Experiments are conducted using different numbers of nodes to determine the total distance moved by nodes and compared the values obtained in SDDA, RAND, and CDA. The result is provided in Figure 8. Evidently, the total distance moved in the three algorithms increases with the number of nodes. When the number of nodes is less than 30, the total distance of CTDA is the smallest under the same number of nodes; when the number of nodes is greater than 30, the total distance moved in CTDA is greater than that in RAND, but is always smaller than those in SDDA and CDA. The reason for this result is that when there are only few nodes in CTDA, the constructed tree is relatively small, and only nodes that move a short distance will reach the calculated position. As the number of nodes increases, the sub-tree gradually grows taller; thus, the node should move at a longer distance, which increases the total distance. For CDA, the location of each node is selected from above or below the deployed nodes; thus, the span of the candidate positions is extensive. If too many nodes are deployed below the deployed nodes, then the moving distance of the nodes increases substantially. In SDDA, some nodes require continuous depth adjustment after the first round to reduce overlap or improve connectivity. When the number of nodes increases, total travel distance also increases.


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

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

Comparison of total moved distance at varying numbers of nodes.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16763-f008: Comparison of total moved distance at varying numbers of nodes.
Mentions: A 3D coverage is obtained by adjusting the depth of nodes. Obviously, the longer the distance moved by nodes, the greater the amount of energy consumed. Experiments are conducted using different numbers of nodes to determine the total distance moved by nodes and compared the values obtained in SDDA, RAND, and CDA. The result is provided in Figure 8. Evidently, the total distance moved in the three algorithms increases with the number of nodes. When the number of nodes is less than 30, the total distance of CTDA is the smallest under the same number of nodes; when the number of nodes is greater than 30, the total distance moved in CTDA is greater than that in RAND, but is always smaller than those in SDDA and CDA. The reason for this result is that when there are only few nodes in CTDA, the constructed tree is relatively small, and only nodes that move a short distance will reach the calculated position. As the number of nodes increases, the sub-tree gradually grows taller; thus, the node should move at a longer distance, which increases the total distance. For CDA, the location of each node is selected from above or below the deployed nodes; thus, the span of the candidate positions is extensive. If too many nodes are deployed below the deployed nodes, then the moving distance of the nodes increases substantially. In SDDA, some nodes require continuous depth adjustment after the first round to reduce overlap or improve connectivity. When the number of nodes increases, total travel distance also increases.

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.