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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 distances moved with varying communication radii under a sparse environment.
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sensors-15-16763-f011: Comparison of distances moved with varying communication radii under a sparse environment.

Mentions: Meanwhile, the total distances moved in the four algorithms under a sparse environment are compared. The results are presented in Figure 11, which indicates that the total distance moved in RAND and CDA is the largest, with fluctuations of approximately 1.5 × 104 m, which is roughly half the water depth multiplied by the number of sensor nodes. The total distance moved in SDDA is less than those in RAND and CDA, and slowly decreases as communication radius increases; the total distance moved in CTDA is the shortest. Given that a node moves randomly in RAND, many unnecessary movements occur. However, given the large communication radius of CDA in a sparse environment, this algorithm widens the span of node adjustment and increases moving distance. In SDDA, when the communication radius of a node increases, the number of cluster members also increases, which may raise the group number, reduce interlayer distance, and shorten the moving distance of some nodes. In CTDA, the distance moved by nodes can be reduced effectively by gradually adjusting the distance between the root and child nodes within a tree. A high adjustment level indicates that good performance is achieved but the communication energy cost of the root node will be high.


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

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

Comparison of distances moved 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-f011: Comparison of distances moved with varying communication radii under a sparse environment.
Mentions: Meanwhile, the total distances moved in the four algorithms under a sparse environment are compared. The results are presented in Figure 11, which indicates that the total distance moved in RAND and CDA is the largest, with fluctuations of approximately 1.5 × 104 m, which is roughly half the water depth multiplied by the number of sensor nodes. The total distance moved in SDDA is less than those in RAND and CDA, and slowly decreases as communication radius increases; the total distance moved in CTDA is the shortest. Given that a node moves randomly in RAND, many unnecessary movements occur. However, given the large communication radius of CDA in a sparse environment, this algorithm widens the span of node adjustment and increases moving distance. In SDDA, when the communication radius of a node increases, the number of cluster members also increases, which may raise the group number, reduce interlayer distance, and shorten the moving distance of some nodes. In CTDA, the distance moved by nodes can be reduced effectively by gradually adjusting the distance between the root and child nodes within a tree. A high adjustment level indicates that good performance is achieved but the communication energy cost of the root node will be high.

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.