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An Improved Localization Scheme Based on PMCL Method for Large-Scale Mobile Wireless Aquaculture Sensor Networks

  • Research Article - Computer Engineering and Computer Science
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Abstract

Localization is crucial to many applications in wireless sensor networks (WSNs) because measurement data or information exchanges happened in WSNs without location information are meaningless. Most localization schemes for mobile WSNs are based on Sequential Monte Carlo (SMC) algorithm. These SMC-based methods often suffer from too many iterations, sample impoverishment and less sample diversity, which leads to low sampling and filtering efficiency, and consequently low localization accuracy and high localization costs. In this paper, we propose an improved range-free localization scheme for mobile WSNs based on improved Population Monte Carlo localization (PMCL) method, accompanying with Hidden Terminal Couple scheme. A population of probability density functions is proposed to approximate the distribution of unknown locations based on a set of observations through an iterative importance sampling procedure. Behaviors are enhanced by adopting three improved methods to increase accuracy, enhance delay and save cost. Firstly, resampling, with importance weights, is introduced in PMCL method to avoid sample degeneracy. Secondly, twofold constraints, constraining the number of random samples in initialized step and constraining valid observations in resampling step, are proposed to decrease the number of iterations. Thirdly, mixture perspective is introduced to maintain the diversity of samples in resampling weighted process. Then, localization error, delay and consumption, especial delay, are predicted based on the statistic point of view, which takes mobile model of RWP into account. Moreover, performance comparisons of PMCL with other SMC-based schemes are also proposed. Simulation results show that delay of PMCL has some superiorities to that of other schemes, and accuracy and energy consumption is improved in some cases of less anchor rate and lower mobile velocity.

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Authors and Affiliations

  1. SOU College of Engineering Science and Technology, Shanghai Ocean University, No.999, Huchenghuan Rd, Shanghai, People’s Republic of China

    Chunfeng Lv & Jian** Zhu

  2. Department of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, No. 800, Dongchuan Road, Shanghai, 200240, China

    Zhengsu Tao

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  1. Chunfeng Lv
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  2. Jian** Zhu
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  3. Zhengsu Tao
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Correspondence to Jian** Zhu.

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Lv, C., Zhu, J. & Tao, Z. An Improved Localization Scheme Based on PMCL Method for Large-Scale Mobile Wireless Aquaculture Sensor Networks. Arab J Sci Eng 43, 1033–1052 (2018). https://doi.org/10.1007/s13369-017-2871-x

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  • DOI: https://doi.org/10.1007/s13369-017-2871-x

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