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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.


Coverage comparison with varying numbers of nodes.
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sensors-15-16763-f004: Coverage comparison with varying numbers of nodes.

Mentions: First, Rc is set to 17.9 m and Rb to 10 m. Then, the coverage performance of CTDA is assessed and compared with those of SDDA, RAND, and CDA under different nodes. The results are presented in Figure 4. Evidently, coverage increases as the number of nodes increases for all the aforementioned algorithms. Using the same number of nodes, the coverage values of CTDA, SDDA, and CDA are close to one another and are higher than that of RAND. This result is attributed to the fact that the cluster head in SDDA assigns a group number based on whether a coverage overlap exists between two nodes. If an overlap exists, then the two nodes are assigned with different group numbers. After adjustment, a coverage overlap may still occur between two neighboring nodes, which, in this case, indicates that the depth of the nodes should be continuously adjusted to further reduce overlap and improve overall coverage. In CTDA, the root node selects the leaf node, which has the greatest coverage overlap with its parent node as a child node. Adjusting the distance between the two nodes minimizes overlap under the premise of maintaining connectivity to maximize coverage. CDA initially constructs the connected backbone, and then uses the dominating node on the backbone to calculate the depth of the dominated node of the backbone iteratively. The process can be described as follows. First, some possible depths of a dominated node are determined by the dominating node. In addition, the corresponding coverage that overlaps between the dominated node and the other deployed nodes at different depths of the dominated node is also calculated. Finally, the depth that corresponds to the minimum coverage overlap is selected as the final depth of the dominated node. In RAND, the depths of the nodes are randomly adjusted, which may expand the coverage overlap area between nodes.


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

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

Coverage comparison with varying numbers of nodes.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16763-f004: Coverage comparison with varying numbers of nodes.
Mentions: First, Rc is set to 17.9 m and Rb to 10 m. Then, the coverage performance of CTDA is assessed and compared with those of SDDA, RAND, and CDA under different nodes. The results are presented in Figure 4. Evidently, coverage increases as the number of nodes increases for all the aforementioned algorithms. Using the same number of nodes, the coverage values of CTDA, SDDA, and CDA are close to one another and are higher than that of RAND. This result is attributed to the fact that the cluster head in SDDA assigns a group number based on whether a coverage overlap exists between two nodes. If an overlap exists, then the two nodes are assigned with different group numbers. After adjustment, a coverage overlap may still occur between two neighboring nodes, which, in this case, indicates that the depth of the nodes should be continuously adjusted to further reduce overlap and improve overall coverage. In CTDA, the root node selects the leaf node, which has the greatest coverage overlap with its parent node as a child node. Adjusting the distance between the two nodes minimizes overlap under the premise of maintaining connectivity to maximize coverage. CDA initially constructs the connected backbone, and then uses the dominating node on the backbone to calculate the depth of the dominated node of the backbone iteratively. The process can be described as follows. First, some possible depths of a dominated node are determined by the dominating node. In addition, the corresponding coverage that overlaps between the dominated node and the other deployed nodes at different depths of the dominated node is also calculated. Finally, the depth that corresponds to the minimum coverage overlap is selected as the final depth of the dominated node. In RAND, the depths of the nodes are randomly adjusted, which may expand the coverage overlap area between nodes.

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.