A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao, Qi; Zhu, R. Y.

doi:10.1186/s13638-018-1037-1

Cited by 23 publications

(26 citation statements)

References 19 publications

(13 reference statements)

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“…However, the localization coverage of the algorithm needs to be improved. In Reference [18], the differential evolution algorithm and the Monte Carlo node localization algorithm are combined to improve the positioning accuracy. However, the use of hardware equipment to measure the distance leads to large energy consumption of the whole network and reduces the life cycle of the network.…”

Section: Research Status Of Monte Carlo Node Localizationmentioning

confidence: 99%

Monte Carlo Node Localization Based on Improved QUARTE Optimization

et al. 2021

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Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

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Section: Research Status Of Monte Carlo Node Localizationmentioning

confidence: 99%

Monte Carlo Node Localization Based on Improved QUARTE Optimization

et al. 2021

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“…The MCL process is mainly divided into the prediction phase, the filtering phase, the resampling phase, and the importance sampling phase. The algorithm steps are as follows [19,20].…”

Section: Basic Principles Of MCLmentioning

confidence: 99%

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

2021

Wireless Communications and Mobile Computing

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In order to improve the positioning accuracy of underground targets, especially the positioning accuracy of moving targets, an improved weighted Monte Carlo positioning algorithm is proposed. In the sampling initialization stage, the beacon node gradually constructs the sampling area according to the RSSI size and combines the Monte Carlo method to further narrow the range and improve the sampling success rate. In the filtering stage, refer to the sampling area at time and further improve the sample quality at t − 1 after two filterings. In the recollection stage, cooperate with invalid sample sets to reduce the number of recollections and weigh the final samples to improve the positioning accuracy of the nodes to be tested.

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“…Differential evolution has been used as a node localization algorithm whose solitary role is to find the optimal position of sensors with minimal cost [34]. Coverage area maximization is one major area where DE has consistently been applied in wireless networks.…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

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A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Cited by 23 publications

References 19 publications

Monte Carlo Node Localization Based on Improved QUARTE Optimization

Monte Carlo Node Localization Based on Improved QUARTE Optimization

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

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