A Variable Weight Based Fuzzy Data Fusion Algorithm for WSN

Wang, Qianping; Liao, Hongmei; Wang, Ke; Sang, Yuan

doi:10.1007/978-3-642-23641-9_39

Cited by 7 publications

(11 citation statements)

References 12 publications

(13 reference statements)

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“…Besides, the microcontroller continuously sent the output data to a computer by a serial cable to measure and plot the performance. The results have been compared with a variable weight based fuzzy data fusion algorithm for WSN proposed by Wang et al in [27] because it is a distributed fuzzy-based data fusing algorithm that enhances the QoS in WSNs, similar to the one introduced in this paper. Moreover, the proposed solution has also been compared with the fuzzy-based data fusion approaches presented in [28] by Wang et al and in [29] by Shell et al However, it is necessary to note that in the latter two methods the power consumption of sensor nodes is not addressed, and the authors do not consider the QoS as the main requirement for their application.…”

Section: Resultsmentioning

confidence: 99%

“…More in detail, the solution proposed by the authors improves the correction precision, but both the power consumption and data compression are not taken into account. Still in the research field of structurefree data fusion approaches, in [27] a data fusion approach for WSNs, based on variable weight fuzzy inference system, is suggested by the authors. The goal is to enhance the reliability and the efficiency of the data fusion mechanism in a Wireless Sensor Network.…”

Section: Structured-based and Free-based Fusion Methodsmentioning

confidence: 99%

“…The authors of [27] suggest a new fuzzy data fusion algorithm, based on variable weight, for WSNs to enhance the reliability and efficiency of the global data fusion method. In the algorithm proposed by the authors, the weight of all cluster head nodes is not fixed in the entire fusion mechanism.…”

Section: Fuzzy-based Data Fusion Approachesmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Resultsmentioning

confidence: 99%

Section: Structured-based and Free-based Fusion Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…For that purpose, we develop a T2 FLS to find the most appropriate CHs for the clusters. As in previous works [26][27][28][29], the sensor nodes will be embedded with a fuzzy system. For each input synthetic data is used.…”

Section: Self-configurable Clusteringmentioning

confidence: 99%

An Alternative Clustering Scheme in WSN

2015

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Despite significant advancements in wireless sensor networks (WSNs), energy conservation in the networks remains one of the most important research challenges. One approach commonly used to prolong the network lifetime is through aggregating data at the cluster heads (CHs). However, there is possibility that the CHs may fail and function incorrectly due to a number of reasons such as power instability. During the failure, the CHs are unable to collect and transfer data correctly. This affects the performance of the WSN. Early detection of failure of CHs will reduce the data loss and provide possible minimal recovery efforts. This paper proposes a self-configurable clustering (SCCH) mechanism to detect the disordered CHs and replace them with other nodes. Simulation results verify the effectiveness of the proposed approach.

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“…A grid‐based data aggregation scheme (GBDAS) divides the experimental area into various cells, where each cell has a sensor node, probably the one with the maximum residual energy capable of fusing neighbor nodes data in addition to its own . A Kalman filter‐based approach was used to detect and rectify outliers, ie, noisy data, generated by a malfunctioning sensor node . Pradhan et al described the potentials of truncated bits procedure that drops extra bits of redundant data without losing any information, in resolving the redundancy issue that is tightly coupled with nodes reside in closed proximity.…”

Section: Literature Reviewmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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A Variable Weight Based Fuzzy Data Fusion Algorithm for WSN

Cited by 7 publications

References 12 publications

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

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

An Alternative Clustering Scheme in WSN

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

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