Limits...
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 under a sparse environment.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541905&req=5

sensors-15-16763-f010: Connectivity with varying communication radii under a sparse environment.

Mentions: Figure 10 presents the comparison of the four algorithms in terms of connectivity under a sparse environment. As shown in the figure, the connectivity of CTDA always maintains a high position. This position further proves the effectiveness of CTDA. In Figure 10, SDDA performs the worst because as the network becomes sparse, the number of nodes with a coverage overlap with neighboring nodes is reduced, an increasing number of nodes are assigned with the same group number, and the distance between two groups widens. If the communication radius is smaller than the inner-group distance, then the connection between two groups fails, and the whole network becomes disconnected in the worst-case scenario. Therefore, to improve connectivity, node depth requires further adjustment, which increases energy consumption.


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 under a sparse environment.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16763-f010: Connectivity with varying communication radii under a sparse environment.
Mentions: Figure 10 presents the comparison of the four algorithms in terms of connectivity under a sparse environment. As shown in the figure, the connectivity of CTDA always maintains a high position. This position further proves the effectiveness of CTDA. In Figure 10, SDDA performs the worst because as the network becomes sparse, the number of nodes with a coverage overlap with neighboring nodes is reduced, an increasing number of nodes are assigned with the same group number, and the distance between two groups widens. If the communication radius is smaller than the inner-group distance, then the connection between two groups fails, and the whole network becomes disconnected in the worst-case scenario. Therefore, to improve connectivity, node depth requires further adjustment, which increases energy consumption.

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.