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Sensors 2017, 17(2), 316; doi:10.3390/s17020316

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks

1
Department of Information and Communication Systems, Hohai University, 200 North Jinling Road, Changzhou 213022, China
2
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
3
School of Information Science and Engineering, Shenyang Ligong University, China
*
Author to whom correspondence should be addressed.
Received: 9 January 2017 / Revised: 6 February 2017 / Accepted: 6 February 2017 / Published: 8 February 2017
(This article belongs to the Special Issue Advances and Challenges in Underwater Sensor Networks)
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Abstract

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency. View Full-Text
Keywords: Underwater Acoustic Sensor Networks; data collection; probabilistic neighborhood Underwater Acoustic Sensor Networks; data collection; probabilistic neighborhood
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MDPI and ACS Style

Han, G.; Li, S.; Zhu, C.; Jiang, J.; Zhang, W. Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks. Sensors 2017, 17, 316.

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