Abstract
Wireless Sensor Networks (WSNs) have been envisioned as the emerging technology and applied widely, but they also faced many practical challenges. One of such challenges is the coverage issue because of a high coverage rate ensures a high quality of service of the WSN. This paper proposes a novel method to optimize sensor coverage based on the enhanced diversity herds grey wolf optimizer (EGWO). In the proposed method, coverage overlaps and holes of deploying WSN are considered to a mathematical model for the objective function of the optimization problem. Quality performance of the proposed method is evaluated through simulation in several scenarios of WSN. The simulation results compared with other methods such as the grey wolf optimizer and the genetic algorithm shows that the proposed algorithm achieves a good coverage and a competitor.
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Acknowledgments
We are grateful to the support from Ministry of Science and Technology, Taiwan, R.O.C. to this study, under the grant numbers MOST 104-2221-E-151-068- and MOST 105-2221-E-151-007-.
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Shieh, CS., Nguyen, TT., Wang, HY., Dao, TK. (2017). Enhanced Diversity Herds Grey Wolf Optimizer for Optimal Area Coverage in Wireless Sensor Networks. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_21
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DOI: https://doi.org/10.1007/978-3-319-48490-7_21
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