Improved range-free localization for three-dimensional wireless sensor networks using genetic algorithm

Sharma, Gaurav; Kumar, Ashok

doi:10.1016/j.compeleceng.2017.12.036

Cited by 73 publications

(43 citation statements)

References 16 publications

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“…The complexity of an algorithm can be measured by calculating its computational cost . Communication cost is represented by the total number of message transmitted and received by the sensor nodes during the localization process.…”

Section: Resultsmentioning

confidence: 99%

“…At last, particle swarm optimization is used to minimizes the localization error of the algorithm. However, anchor nodes placement at the boundary of sensing field is not feasible for isolated area . This paper proposed an improved range‐free localization using genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In the first step, each anchor node calculates coordinates of its position using GPS device and broadcast their location in the network . The hop count value is initially set to 0.…”

Section: Traditional Dv‐hop Localization Using Multiobjective Gamentioning

confidence: 99%

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Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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Summary Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Kanwar

Kumar

2020

Int J Communication

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“…The target node positioning algorithm can be built in several ways. A triangle centroid positioning algorithm based on the distance or relative angle information between the target node and the anchor node has been proposed in the document [17][18][19]. There are problems of the target node deviating effective locating area and the large positioning error in the positioning algorithm because the node distribution characteristics are not entirely considered.…”

Section: The Target Node Positioning Algorithmmentioning

confidence: 99%

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Chen¹,

Wang²,

Ouyang³

et al. 2019

Preprint

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The node position information is critical in the wireless sensor network (WSN). However, the existing positioning algorithms commonly have low positioning accuracy because of noise interferences in communication. To solve this problem, this paper presents an iterative positioning model based on distance correction to improve the positioning accuracy of the target node in WSN. First, the log-distance distribution model of received signal strength indication (RSSI) ranging is built and the noise impact factor is derived based on the model. Second, the initial position coordinates of the target node are obtained based on the triangle centroid localization algorithm, thereby calculating the distance deviation coefficient under the influence of noise. Then, the ratio of the distance measured by the log-normal distribution model to the median distance deviation coefficient is taken as the new distance between the anchor node and the target node. Based on the new distance, the triangular centroid positioning algorithm is used again to calculate the target node coordinates. Finally, the iterative positioning model is constructed, and the distance deviation coefficient is updated repeatedly to update the positioning result until the set number of iterations is reached. Experiment results show that the proposed iterative positioning model can improve positioning accuracy effectively.

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“…GA proved to be capable of solving large number of NP hard problems also, including problems from the domain of WSNs 22 . Sharma, G etc proposed a distributed range-free node localization algorithm for three dimensional WSNs based on the GA 23 . Similarly, by applying the localization algorithm that employs GA, the localization accuracy of unknown nodes in WSNs was improved 24 , and a novel range free localization algorithm based on GA and connectivity was proposed recently also 25 .…”

mentioning

confidence: 99%

Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm

Zhang

Deng

Dai

et al. 2020

Sci Rep

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Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer's high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R 2 , 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.

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Improved range-free localization for three-dimensional wireless sensor networks using genetic algorithm

Cited by 73 publications

References 16 publications

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm

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