Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm
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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.
Keywords3D 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.
- Abidin ZZ, Arshad MR, Ngah UK (2011) A simulation based fly optimization algorithm for swarms of mini autonomous surface vehicles application. Indian J Geo Mar Sci 40(2):250–266Google Scholar
- Du X, Sun L, Liu L et al (2013) Coverage optimization algorithm based on sampling for 3D underwater sensor networks. Int J Distrib Sens Netw 42(3):286–291Google Scholar
- Huang J, Sun L, Wei X et al (2014) Redundancy model and boundary effects based coverage-enhancing algorithm for 3D underwater sensor networks. Int J Distrib Sens Netw 7(1):234–244Google Scholar
- Lian X, Zhang J, Chen C et al (2012) Three-dimensional deployment optimization of sensor network based on an improved Particle Swarm Optimization algorithm. In: 2012 10th world congress on intelligent control and automation (WCICA), IEEE 2012, pp 4395–4400Google Scholar
- Pan W (2011) Fruit fly optimization algorithm. Tsang Hai Book Publishing Co., TaipeiGoogle Scholar
- Senel F, Yilmaz T (2013) Autonomous deployment of sensors for maximized coverage and guaranteed connectivity in underwater acoustic sensor networks. In: 2013 IEEE 38th conference on local computer networks (LCN), IEEE, 2013, pp 211–218Google Scholar