A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks

Fang, Kai; Wang, Tingting; Zhou, Xiaolong; Ren, Yaping; Guo, Hongfei; Li, Jianqing

doi:10.1109/tii.2021.3076770

Cited by 33 publications

(8 citation statements)

References 32 publications

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“…Cheng et al [25] based on the generalized probability association algorithm of time arrival and the unknown node estimation with the model probability and covariance matrix to improve the positioning accuracy. Fang et al [26] introduced three indicators to screen out reliable beacon nodes and used them to solve equations for positioning. Kaur [27] et al proposed the weighted centroid algorithm to determine the position of unknown nodes by considering the weights influenced by different factors such as the number of beacon nodes, communication radius, and nearest beacon node.…”

Section: Related Workmentioning

confidence: 99%

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Wang,

Shen,

Zhou

2023

Preprint

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This work proposes an improved DV-Hop model based on function analysisand simulation parameter determination (named FuncDV-Hop) to address theproblems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step functionsegmentation experiment, weight function strategy with equivalent points, andmaximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distanceestimation, and least square calculation of the DV-Hop model. The experiment isdesigned using the control variable method. The total number of nodes, the ratioof beacon nodes, the communication radius, the number of beacon nodes, and thenumber of unknown nodes are designed as variables to control the experiment.Finally, the experiments of two stages of simulation parameter determinationand integration optimization are carried out. The simulation optimization rateof all scenes is between 23.70% and 75.76%, and the average optimization rateis 57.23%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios comparedwith the other models, the localization error is reduced by more than 0.1, andthe optimization rate is increased by more than 10% in the record parameters ofthe existing wireless sensor network system.

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Section: Related Workmentioning

confidence: 99%

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Wang,

Shen,

Zhou

2023

Preprint

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“…, p. To obtain the attribute constraint matrix of the construction waste data, the initial construction waste data set was analyzed. By obtaining the column data type of the dataset, a multi-attribute constraint matrix X was constructed, as shown in Equation (1) [15].…”

Section: Intermediate Indicatormentioning

confidence: 99%

“…Finally, the output of the model is determined, the initial output through a sigmoid layer is obtained, the tanh layer is used to perform the shrinking operation, the sigmoid tanh in pairs is multiplied, and a clean data set is obtained [40][41][42][43]. The o t and h t equations (i.e., (15) and ( 16)) are shown below:…”

Section: Time Series Data Cleaning Modelmentioning

confidence: 99%

Research on Data Cleaning Algorithm Based on Multi Type Construction Waste

Wang

Liu

Sun³

et al. 2022

Sustainability

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Owing to urbanization, the output of construction waste is increasing yearly. Garbage treatment plays a vital role in urban development and construction. The accuracy and integrity of data are important for the implementation of construction waste treatment. Abnormal detection and incomplete filling occur when traditional cleaning algorithms are used. To improve the cleaning of construction waste data, a data cleaning algorithm based on multi-type construction waste was presented in this study. First, a multi-algorithm constraint model was designed to achieve accurate matching between the cleaning content and cleaning model. Thereafter, a natural language data cleaning model was proposed, and the spatial location data were separated from the general data through the content separation mechanism to effectively frame the area to be cleaned. Finally, a time series data cleaning model was constructed. By integrating “check” and “fill”, large-span and large-capacity time series data cleaning was realized. This algorithm was applied to the data collected by the pilot cities, which had precision and recall rates of 93.87% and 97.90% respectively, compared with the traditional algorithm, ultimately exhibiting a certain progressiveness. The algorithm proposed herein can be applied to urban environmental governance. Furthermore, this algorithm can markedly improve the control ability and work efficiency of construction waste treatment, and reduce the restriction of construction waste on the sustainable development of urban environments.

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“…Routing Protocol According to the routing table inside the node, the response data frame is sent back to the BS along the shortest path provided by the Dijkstra algorithm in the form of multi-hops. The response data frame contains the residual information of the node residual energy and node position, assuming that the node position in the network has been obtained by GPS or positioning technology [15,16]. So far, the deployment of WSN has been completed.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

Jiang

Ding

et al. 2022

Mobile Information Systems

Self Cite

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The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.

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A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks

Cited by 33 publications

References 32 publications

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Improved DV-Hop Model Based on theApplication of Variable Scenarios

Research on Data Cleaning Algorithm Based on Multi Type Construction Waste

A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

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