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

Lv, Chunfeng; Zhu, Jianping; Zhang, Tao

doi:10.1007/s13369-017-2871-x

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…At each time interval, state error can be obtained from Eq. 27 referred from [45], in which x iest k is the estimated state and x i k is the actual one, which are the distances between target and sensors.…”

Section: Mc-mpmc-based Tracking Methodsmentioning

confidence: 99%

“…For example, target can obtain its 1-hop and 2-hop neighbors through performing HTC scheme in iteration k . And then it can derive its relative locations to its neighbors [45]. MPMC filter can derive target tracking adopting N k proposals within these locations, accompanied by measurements or observations detected by the target in this iteration k.…”

Section: Problem Statementmentioning

confidence: 99%

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An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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Section: Mc-mpmc-based Tracking Methodsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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“…The prior distribution p 1 ðl k Þ can be obtained using Equation (11), and model evidence or partition function Zðo t Þ is shown in Equation (12).…”

Section: F I G U R Ementioning

confidence: 99%

A localization scheme based on Improving Dynamic Population Monte Carlo Localization method for large‐scale mobile wireless aquaculture sensor networks

Zhu

Chen

2023

IET Wireless Sensor Systems

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Localization is one of the essential problems in wireless sensor applications (WSNs). Most range‐free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be solved in these SMC‐based methods. An improved localization scheme for mobile aquaculture WSNs based on the Improving Dynamic Population Monte Carlo Localization (I‐DPMCL) method is proposed. A population of probability density functions is proposed to approximate the unknown location distribution based on a set of observations through an iterative mixture importance sampling procedure, accompanied by node dynamic behaviours being analysed quantitatively or definitely. Threefold constrain rules are put forward in the I‐DPMCL scheme to decrease the iteration number and trade off iteration number and enough valid samples to obtain the optimum iteration number. Then, these localization behaviours, especial delay, are predicted based on the statistical point of view. Moreover, performance comparisons of I‐DPMCL with other SMC‐based schemes are also proposed. Simulation results show that delay of I‐DPMCL has some superiority to those of other schemes, and accuracy and energy consumption are improved in some cases of lower mobile velocity.

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“…Therefore, the accuracy of the localization is increased significantly. Lv et al [10] proposed a localization scheme for mobile wireless sensor networks (WSNs) based on Population Monte Carlo Localization (PMCL) method. A population of probability density functions is used to estimate the distributions of unidentified locations based on a set of observations through an iterative procedure.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Iterative Clustering for Energy-Efficient Large-Scale Tracking Systems

Alfares

Elkhail

Baroudi

2019

Wireless Pers Commun

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A new technique is presented to design energy-efficient large-scale tracking systems based on mobile clustering. The new technique optimizes the formation of mobile clusters to minimize energy consumption in large-scale tracking systems. This technique can be used in large public gatherings with high crowd density and continuous mobility. Utilizing both Bluetooth and Wi-Fi technologies in smart phones, the technique tracks the movement of individuals in a large crowd within a specific area, and monitors their current locations and health conditions. The new system has several advantages, including good positioning accuracy, low energy consumption, short transmission delay, and low signal interference. Two types of interference are reduced: between Bluetooth and Wi-Fi signals, and between different Bluetooth signals. An integer linear programming model is developed to optimize the construction of clusters. In addition, a simulation model is constructed and used to test the new technique under different conditions. The proposed clustering technique shows superior performance according to several evaluation criteria.

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

Cited by 7 publications

References 39 publications

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

A localization scheme based on Improving Dynamic Population Monte Carlo Localization method for large‐scale mobile wireless aquaculture sensor networks

Iterative Clustering for Energy-Efficient Large-Scale Tracking Systems

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