Application of Fuzzy Data Fusion in Multi-sensor Environment Monitor

Sun, Haitao; Wang, Weisheng; Cao, Yi; He, Shenxue; Yan, Xin

doi:10.1109/cis.2009.257

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The fuzzy membership degree discussed above reflects the proximity between the measured data and the real value of the sensor, so it can be used as the weight. The formula (2) shows that the membership degree is the key to the fuzzy weighted fusion method. Delphi method, fuzzy statistics method, increment method and factor weighting synthesis method are the main methods to solve the membership degree [10].…”

Section: Fuzzy Weighted Fusion Methodsmentioning

confidence: 99%

“…The sensor data fusion algorithm includes the least squares algorithm, fuzzy weighted fusion algorithm , Calman filtering algorithm [2], clustering algorithm [3], neural network algorithm [4], template method [5] and D-S evidence reasoning algorithm [6] and so on, as shown in table 1. Fuzzy weighted fusion algorithm is to use fuzzy mathematics method for determining the weights of sensors in data fusion, and with strong applicability, moderate calculation cost and high precision, it is commonly used in the data level fusion, especially for homogeneous data.…”

Section: Introductionmentioning

confidence: 99%

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The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude

Qu¹,

He²,

Zhang³

2017

Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

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Abstract. In order to improve the accuracy and reliability of the multi-sensor data fusion, a new modified reciprocal fuzzy neartude based approach to calculate the weights of the fusion model is proposed. Through the research of the fusion model, it is found that the fuzzy neartude is more practical than fuzzy membership for the calculation of the weights. The fusion performance of the five types of fuzzy neartude is analyzed, and the reciprocal fuzzy neartude is proved to be one of the best for the resolution and mount of calculation. However, the neartude cannot suppress the singular data well. To address the problem, the reciprocal fuzzy neartude is modified. The simulation analysis shows that the modified reciprocal fuzzy neartude based approach can fuse the multi-sensor data with high accuracy and reliability.

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Section: Fuzzy Weighted Fusion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude

Qu¹,

He²,

Zhang³

2017

Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

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“…Gupta et al ’s (2005) technique is modified and further developed in Tashtoush and Okour (2008), Haider and Yusuf (2009), Sun et al (2009), Raghuvanshi et al (2010), Ran et al (2010), Taheri et al (2010) and Biglarbegian et al (2011). These techniques have used different input variables to calculate the chance value using the fuzzy logic system.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

Singh

Soni

2017

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Purpose This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed. Design/methodology/approach The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes. Findings This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols. Originality/value This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.

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“…A method based on fuzzy-evaluation for data fusion, implemented to an environmental monitoring system with multisensors, is introduced in [26]. At first, the amount of the humidity, temperature, ventilation, and other parameters, collected by multiple sensors, is fuzzed.…”

Section: Fuzzy-based Data Fusion Approachesmentioning

confidence: 99%

A Fuzzy Data Fusion Solution to Enhance the QoS and the Energy Consumption in Wireless Sensor Networks

Collotta

Pau

Bobovich

2017

Wireless Communications and Mobile Computing

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Wireless Sensor Networks (WSNs) are formed of various nodes that gather parameters in a monitored environment. These nodes interact with each other or can be arranged into clusters controlled by a cluster head that has the task of rerouting the acquired data to a base station. Both the Quality of Service (QoS) and low data quality are common issues in WSNs, mainly prompted by the data fusion mechanism, where a certain amount of low-quality data may affect the overall fusion result negatively. In this paper, a fuzzy-based solution for data fusion in WSNs is presented to provide a better QoS and to reduce the energy consumption. The suggested approach can aggregate only true value rather than process the full data. This purpose is accomplished thanks to a Fuzzy Logic Controller (FLC) implemented within nodes. Besides, the data, which have been separated, are aggregated by a cluster head which also has the responsibility of determining the probability that an event has happened in the monitored environment. Finally, the base station estimates whether an event has occurred and, eventually, raises an appropriate alarm. The results of a real testbed scenario reveal that the proposed method achieves encouraging performance.

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Application of Fuzzy Data Fusion in Multi-sensor Environment Monitor

Cited by 7 publications

References 6 publications

The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude

The Algorithm of Multi-sensor Data Fusion Based on the Modified Reciprocal Fuzzy Neartude

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

A Fuzzy Data Fusion Solution to Enhance the QoS and the Energy Consumption in Wireless Sensor Networks

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