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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 varies with time under a dynamic environment.
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sensors-15-16763-f012: Connectivity varies with time under a dynamic environment.

Mentions: Figure 12 shows the comparison chart when the initial deployment area is 35.7 m × 35.7 m × 53.9 m and the number of nodes is 43. Evidently, when the value of Rc varies, the connectivity of the four algorithms decreases with time at different rates. When Rc is 10 m, network connectivity declines the fastest, whereas network connectivity declines the slowest when Rc is 30 m. This result is attributed to the fact that when Rc is small, minimal changes in position cause a rapid decline in connectivity. When Rc is 10 m, the connectivity of CTDA decreases the fastest in the initial period. To maximize network coverage when Rc is less than 2 × Rs, the distance between the parent and child nodes is set as equal to Rc. Thus, the connectivity between the root and child nodes is vulnerable. The distance can be reduced by sacrificing certain network coverage to improve connectivity quality among nodes. When Rc gradually increases from 10 m to 30 m, the effect of position change on network connectivity is decelerated.


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

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

Connectivity varies with time under a dynamic environment.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16763-f012: Connectivity varies with time under a dynamic environment.
Mentions: Figure 12 shows the comparison chart when the initial deployment area is 35.7 m × 35.7 m × 53.9 m and the number of nodes is 43. Evidently, when the value of Rc varies, the connectivity of the four algorithms decreases with time at different rates. When Rc is 10 m, network connectivity declines the fastest, whereas network connectivity declines the slowest when Rc is 30 m. This result is attributed to the fact that when Rc is small, minimal changes in position cause a rapid decline in connectivity. When Rc is 10 m, the connectivity of CTDA decreases the fastest in the initial period. To maximize network coverage when Rc is less than 2 × Rs, the distance between the parent and child nodes is set as equal to Rc. Thus, the connectivity between the root and child nodes is vulnerable. The distance can be reduced by sacrificing certain network coverage to improve connectivity quality among nodes. When Rc gradually increases from 10 m to 30 m, the effect of position change on network connectivity is decelerated.

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.