Soft Computing

, Volume 21, Issue 20, pp 6019–6029 | Cite as

Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

  • Ying Zhang
  • Mingxing Wang
  • Jixing Liang
  • Haiyang Zhang
  • Wei Chen
  • Shengming Jiang


The coverage rate of the underwater sensor networks directly influences on the monitoring efficiency in underwater environment, and it can be effectively improved by adjusting the positions of the mobile nodes reasonably for a 3D underwater sensor network which consists of mobile nodes as underwater robots like Autonomous Underwater Vehicles. An optimal deployment method can quickly set up a reasonable topology of the sensor networks and achieve a higher efficiency for detecting or investigating. An optimal algorithm of coverage enhancing for 3D Underwater sensor networks based on improved Fruit Fly Optimization Algorithm (UFOA) is proposed in this paper. This method realizes the global optimal coverage based on foraging behavior of fruit flies, and it has the features of higher speed of convergence, few parameters to set up and stronger global searching ability. Simulation result indicates that the proposed UFOA method can significantly improve the effective coverage rate of the sensor networks compared with some widely studied PSO and IPSO algorithms.


3D underwater sensor networks Deployment Optimization algorithm Coverage rate Fruit fly 



This work was supported by National Nature Science Foundation of China (No. 61673259), and International Exchanges and Cooperation Projects of Shanghai Science and Technology Committee (No. 15220721800).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Human participants or Animals performed

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Computer ScienceTennessee State UniversityNashvilleUSA

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