, Volume 102, Issue 1, pp 83–104 | Cite as

Data collection from underwater acoustic sensor networks based on optimization algorithms

  • Mingzhi Chen
  • Daqi ZhuEmail author


Due to the unique nature of underwater acoustic communication, data collection from the Underwater Acoustic Sensor Networks (UASNs) is a challenging problem. It has been reported that data collection from the UASNs with the assistance of the autonomous underwater vehicles (AUVs) will be more convenient. The AUV needs to schedule a tour to contact all sensors once, which is a variant of the Traveling Salesman Problem. A hybrid optimization algorithm is proposed for the solution of the problem. The algorithm combines the quantum-behaved particle swarm optimization and improved ant colony optimization algorithms. It is an algorithm with quadratic complexity, which can yield approximate but satisfactory results for the problem. Simulation experiments are carried out to demonstrate the efficiency of the algorithm. Compared to the Self-Organizing Map based (SOM-based) algorithm, it not only plans a shorter tour, but also shortens the distance from the sensor to its closest waypoint. Therefore, the algorithm can reduce the energy required for data transmission since the communication distance drops, and the service life of the sensor can be extended.


Underwater acoustic sensor network (UASN) Prize-collecting traveling salesman problem with neighborhood (PC-TSPN) Quantum-behaved particle swarm optimization (QPSO) Improved ant colony optimization (ACO) 

Mathematics Subject Classification

90-XX 90B36 90C27 



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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Intelligent Maritime Search & Rescue and Underwater VehiclesShanghai Maritime UniversityShanghaiChina

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