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2012 International Conference on Artificial Intelligence, ICAI 2012

作者： Seok, Joon-Hong Lee, Joon-Woo Lee, Ju-Jang Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)1601322178

evolutionary sensor deployment algorithm using the dual population scheme and the multiple overlap measure (ESDA-DPMO) is proposed to solve the full-coverage problem with non-penetrable obstacles. The full-coverage state group (FCSG) and the non-full-coverage state group (NFCSG) find sensor deployment solutions using different fitness functions, mutation operators and selection operators, respectively. Two distinguished search directions keep genetic diversity of sensor deployment solutions and avoid getting stuck in local optimum. In addition, information change between two is well designed for efficient exploration ability. The proposed multiple overlap measure boosts both evolution of FCSG and NFCSG. In the FCSG, by gathering sensors together as much as possible, there is a high probability of reducing redundant sensor without breaking full-coverage state. In contrast, in the NFCSG, by scattering sensors as much as possible to get lower overlap rate, higher coverage rate is obtained using same number of sensors. We perform simulations on 3 virtual maps to verify the proposed ESDA-DPMO as compared to conventional approaches. The results show that the proposed ESDA-DPMO provides full-coverage solutions efficiently.

关键词： Coverage problem Dual population evolutionary sensor deployment Multiple overlap measure

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A Bipopulation-Based evolutionary Algorithm for Solving Full Area Coverage Problems

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IEEE sensorS JOURNAL 2013年第12期13卷 4796-4807页

作者： Seok, Joon-Hong Lee, Joon-Yong Kim, Won Lee, Ju-Jang Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50010 USA Woosong Univ Dept Comp Sci Taejon 110810 South Korea

The full area coverage sensor deployment problem is a challenging issue in wireless sensor networks. We focus on sensor deployment strategies that aim to acquire a full-coverage state with a minimum number of sensors in a predetermined target region that includes non-penetrable obstacles. This paper presents an efficient bipopulation-based evolutionary full area coverage (BEFAC) algorithm that involves a bipopulation structure composed of a full-and partial-coverage populations. Fitness functions, stochastic unary recombination operators, and selection procedures between the two populations are well designed. Through applying the proposed BEFAC, a full-coverage state is acquired with a minimum number of deployed sensors in the target region, which has non-penetrable obstacles, and the algorithm avoids getting caught in local minima. The performance results reveal that BEFAC outperforms the conventional deployment methods in terms of the number of deployed sensors and the number of required fitness evaluations.

关键词： Area coverage problem evolutionary sensor deployment full coverage problem

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